Judges’ project

Quick Reference

Is the difference statistically significant? If the adjusted p-value is below the corrected threshold shown in the table note (e.g., > 0.01 or 0.001), then yes!

How big is the difference? Look at Cohen’s d:

Small: .20–.49
Moderate: .50–.79
Large: .80+

A Note on Interpreting Key Statistical Values (if you want the longer version)

p-value: Generally, a result is considered statistically significant when the p value is less than .05. This p-value represents the probability of finding this result by chance alone. For example, a p-value < .05 means that if there were truly no difference between the two groups, we would expect to observe a result this large or larger less than 5% of the time by chance alone.

Which p-value to use: Please prioritise the p (adjusted) column when interpreting results, where available. The adjusted p-value accounts for the fact that we ran multiple tests at once, which increases the risk of finding something significant by chance. The correction partially addresses this by raising the bar for what counts as significant. For example, if we ran 5 tests, the corrected threshold becomes p < .01 instead of the usual p < .05 to make it harder to call something significant by chance alone.

Cohen’s d: The magnitude of difference is categorised as small (.20–.49), moderate (.50–.79), or large (.80+). The Cohen’s d value tells us how large the difference is in practical terms. For instance, a statistically significant result with a small Cohen’s d (e.g., d = 0.2) means the difference is significant statistically but may not be particularly meaningful in practice. Conversely, a p value of .05 with a large Cohen’s d (e.g., d = 0.9) suggests that the difference between the two groups is significant statistically and large in magnitude.

Descriptive Statistics: Wellbeing Measures by Gender, Court Level, and Region

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Descriptive Statistics by Gender
Group	n	K10 Mean	K10 SD	STSS Mean	STSS SD	SWLS Mean	SWLS SD
Male	302	15.82	5.93	30.59	11.73	24.72	5.84
Female	291	17.83	6.33	34.95	12.07	24.03	5.95
NA	9	18.29	6.10	37.12	12.33	24.00	5.92

Descriptive Statistics by Court Level
Group	n	K10 Mean	K10 SD	STSS Mean	STSS SD	SWLS Mean	SWLS SD
Supreme	122	15.26	5.97	29.90	10.86	25.75	5.40
District	157	16.57	6.59	32.44	12.43	24.79	5.86
Local	268	17.63	6.04	34.04	12.18	23.57	6.11
NA	55	17.06	5.71	34.00	12.53	24.02	5.43

Descriptive Statistics by Location
Group	n	K10 Mean	K10 SD	STSS Mean	STSS SD	SWLS Mean	SWLS SD
Metro	512	16.67	6.27	32.48	12.02	24.42	5.98
Regional	67	18.11	5.87	35.11	12.67	24.17	5.13
NA	23	16.33	5.15	32.57	11.79	24.05	6.32

Judicial Attitudes to Work Scale (JAWS) Stress Scores by Gender, Court Level, and Region

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Mean JAWS Stress Scores by Gender
Group	n	JAWS Stress (Item 1)	JAWS Stress (Item 2)
Female	291	3.30	2.66
Male	302	2.77	2.30
NA	9	3.22	2.78

Mean JAWS Stress Scores by Court Level
Group	n	JAWS Stress (Item 1)	JAWS Stress (Item 2)
District	157	2.85	2.27
Local	268	3.35	2.78
Supreme	122	2.46	2.17
NA	55	3.32	2.38

Mean JAWS Stress Scores by Region
Group	n	JAWS Stress (Item 1)	JAWS Stress (Item 2)
Metro	512	2.94	2.44
Regional	67	3.63	2.76
NA	23	3.48	2.86

Gender Differences across Wellbeing Measures

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Gender Differences in Wellbeing Measures
Scale	Men average (n=302)	Women average (n=291)	t	df	p	p (adjusted)	sig	Cohen’s d
Psychological Distress (K10)	15.817	17.825	-3.959	576.605	0.000	0.000	***	0.328
Secondary Traumatic Stress (STSS)	30.586	34.947	-4.413	575.905	0.000	0.000	***	0.366
Satisfaction with Life (SWLS)	24.717	24.031	1.409	583.865	0.159	0.796		0.116
Note. * p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across all 5 tests (corrected threshold: p < .01).

Gender Differences in JAWS Stress Scores
Scale	Men average (n=302)	Women average (n=291)	t	df	p	p (adjusted)	sig	Cohen’s d
JAWS Stress (Item 1)	2.773	3.296	-5.563	563.640	0	0	***	0.468
JAWS Stress (Item 2)	2.302	2.662	-4.128	559.851	0	0	***	0.348
Note. * p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across all 5 tests (corrected threshold: p < .01).

Note. Box plots display the median (centre line), interquartile range (IQR; box), and 1.5 times the IQR (whiskers). Dots represent outliers falling beyond the whiskers.

Plain text Summary. We compared wellbeing and work-related stress between male and female judicial officers using t-tests. Women reported higher psychological distress and secondary traumatic stress than men, while life satisfaction was similar between the two groups. Women also reported higher work-related stress on both JAWS items. These differences held up even after we applied a statistical correction to account for running multiple tests, which gives us a bit more confidence that the findings are reliable rather than due to chance.

Summary w/Statistics. We conducted independent samples t-tests to examine gender differences across wellbeing and JAWS stress items. We also applied Bonferroni correction to control for Type I error because of how many tests were performed. The results in the 1st and 2nd tables above show that women reported significantly higher psychological distress (K10; t(576.60) = -3.96, p < .001, d = 0.33) and secondary traumatic stress (STSS; t(575.90) = -4.41, p < .001, d = 0.37) than men, with no significant difference in life satisfaction (SWLS; t(583.87) = 1.41, p = .16, d = 0.12). Women also reported significantly higher scores on both JAWS stress items (Item 1: t(563.64) = -5.56, p < .001, d = 0.47; Item 2: t(559.85) = -4.13, p < .001, d = 0.35). All the significant results remained significant after correction, which tells us that women judicial officers tended to experience greater psychological distress, secondary traumatic stress, and work-related stress than their male counterparts, even after controlling for Type I error.

Limitations. We ran five inferential tests across several variables in this section. One problem that oftentimes arises from running multiple tests at once is that we’re more likely to find something that looks significant just by chance (i.e., Type I error or false positives). We tried to address this statistically using Bonferroni correction, which essentially works by raising the bar for what counts as significant. In other words, instead of the usual threshold of p < .05, we divide that by the number of tests run (since we have 5 tests, the corrected threshold was p < .01) to make it harder to find something significant by chance alone. However, this only reduces the risk of false positives but does not completely remove it, so we still recommend treating these findings with caution and avoiding drawing strong conclusions.

Court Level Differences across Wellbeing Measures

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Mean Wellbeing Scores by Court Level with ANOVA Results
Outcome	Supreme (n=122)	District (n=157)	Local (n=268)	F	df1	df2	p	p_adj	sig	Eta2
Psychological Distress (K10)	15.256	16.573	17.627	12.401	1	539	0.000	0.002	**	0.022
Secondary Trauma (STSS)	29.901	32.440	34.038	9.865	1	535	0.002	0.009	**	0.018
Satisfaction with Life (SWLS)	25.752	24.790	23.572	12.271	1	540	0.000	0.002	**	0.022
Note. * p < .05, p < .01, * p < .001. p_adj reflects Bonferroni correction across all 5 tests (corrected threshold: p < .01).

Mean JAWS Stress Scores by Court Level with ANOVA Results
Outcome	Supreme (n=122)	District (n=157)	Local (n=268)	F	df1	df2	p	p_adj	sig	Eta2
JAWS Stress (Item 1)	2.461	2.846	3.349	58.228	1	520	0	0	***	0.101
JAWS Stress (Item 2)	2.167	2.272	2.778	34.475	1	516	0	0	***	0.063
Note. * p < .05, p < .01, * p < .001. p_adj reflects Bonferroni correction across all 5 tests (corrected threshold: p < .01).

Tukey HSD Post-Hoc Tests: Wellbeing by Court Level
Outcome	Comparison	Mean Difference	Lower CI	Upper CI	p (adjusted)	sig
Psychological Distress (K10)	District vs Supreme	1.317	-0.443	3.077	0.185
	Local vs Supreme	2.371	0.773	3.970	0.002	**
	Local vs District	1.054	-0.414	2.522	0.211
Secondary Trauma (STSS)	District vs Supreme	2.539	-0.897	5.976	0.193
	Local vs Supreme	4.137	1.053	7.221	0.005	**
	Local vs District	1.598	-1.274	4.469	0.392
Satisfaction with Life (SWLS)	District vs Supreme	-0.962	-2.635	0.711	0.367
	Local vs Supreme	-2.180	-3.698	-0.662	0.002	**
	Local vs District	-1.218	-2.612	0.176	0.101

Tukey HSD Post-Hoc Tests: JAWS Stress by Court Level
Outcome	Comparison	Mean Difference	Lower CI	Upper CI	p (adjusted)	sig
JAWS Stress (Item 1)	District vs Supreme	0.385	0.069	0.700	0.012
	Local vs Supreme	0.888	0.603	1.173	0.000	***
	Local vs District	0.503	0.242	0.765	0.000	***
JAWS Stress (Item 2)	District vs Supreme	0.105	-0.194	0.405	0.686
	Local vs Supreme	0.612	0.341	0.882	0.000	***
	Local vs District	0.506	0.258	0.754	0.000	***

Note. Box plots display the median (centre line), interquartile range (IQR; box), and 1.5 times the IQR (whiskers). Dots represent outliers falling beyond the whiskers.

Plain text Summary. We compared psychological distress, secondary traumatic stress, satisfaction with life, and work-related stress across three court levels (Supreme, District, and Local) to see whether judicial officers in different courts reported different wellbeing and stress levels.

Our results showed that Local court judicial officers reported the highest levels of psychological distress, secondary traumatic stress, and work-related stress, and the lowest satisfaction with life.

Our follow-up (i.e., post-hoc) tests assessing which specific court level pairs differed from each other showed that the differences were most pronounced between Local and Supreme Court officers. Local court officers reported significantly higher distress, secondary trauma, and work stress, and lower life satisfaction than their Supreme Court counterparts. District and Supreme Court officers did not differ significantly from each other on any measure, and neither did Local and District Court officers on the wellbeing measures. However, Local court officers did report significantly higher work-related stress than both Supreme and District Court officers.

Summary w/Statistics. We conducted one-way ANOVAs to examine differences in wellbeing and JAWS stress scores across court levels: Supreme, District, and Local. We then ran the Tukey HSD post-hoc tests to identify which specific court level pairs differed significantly. We also applied Bonferroni correction across all five ANOVA tests to control for Type I error (corrected threshold for 5 tests = p < .01). The results show that Local court judicial officers reported significantly higher psychological distress (K10; F(1, 539) = 12.40, p < .001), higher secondary traumatic stress (STSS; F(1, 535) = 9.87, p = .002), and lower satisfaction with life (SWLS; F(1, 540) = 12.27, p < .001) compared to Supreme and District court officers, with a consistent pattern of increasing distress from Supreme to District to Local court. Local court officers also reported significantly higher scores on both JAWS stress items (Item 1: F(1, 520) = 58.23, p < .001; Item 2: F(1, 516) = 34.48, p < .001).

Post-hoc tests clarified that for wellbeing outcomes, significant differences were found only between Local and Supreme Court officers. Local court officers reported significantly higher psychological distress (mean difference = 2.37, p = .002), higher secondary traumatic stress (mean difference = 4.14, p = .005), and lower satisfaction with life (mean difference = -2.18, p = .002) than Supreme Court officers. No significant differences were found between District and Supreme Court officers, or between Local and District Court officers, on any wellbeing measure. For JAWS stress, Local court officers reported significantly higher stress than both Supreme Court officers (Item 1: mean difference = 0.89, p < .001; Item 2: mean difference = 0.61, p < .001) and District Court officers (Item 1: mean difference = 0.50, p < .001; Item 2: mean difference = 0.51, p < .001), while District and Supreme Court officers did not differ significantly from one another on either item.

Limitations. We also ran five inferential tests across several variables in this section. One problem that oftentimes arises from running multiple tests at once is that we’re more likely to find something that looks significant just by chance (i.e., Type I error or false positives). We tried to address this statistically using Bonferroni correction, which essentially works by raising the bar for what counts as significant. Instead of the usual threshold of p < .05, we divide that by the number of tests run (since we have 5 tests, the corrected threshold was p < .01) to make it harder to call something significant by chance alone. Tukey HSD further controls for familywise error rate across the three pairwise comparisons within each outcome. However, these corrections only reduce the risk of false positives but do not completely remove it, so we still recommend treating these findings with caution and avoiding drawing strong conclusions.

Regional Differences across Wellbeing Measures

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Location Differences in Wellbeing Measures
Scale	Metro (n=512)	Regional (n=67)	t	df	p	p (adjusted)	Cohen’s d
Psychological Distress (K10)	16.666	18.106	-1.858	85.534	0.067	0.333	0.231
Secondary Trauma (STSS)	32.480	35.106	-1.592	81.132	0.115	0.577	0.217
Satisfaction with Life (SWLS)	24.418	24.167	0.367	89.666	0.714	1.000	0.043
Note. * p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across all 5 tests (corrected threshold: p < .01).

Location Differences in JAWS Stress Scores
Scale	Metro (n=512)	Regional (n=67)	t	df	p	p (adjusted)	sig	Cohen’s d
JAWS Stress (Item 1)	2.943	3.629	-4.962	81.513	0.000	0.000	***	0.614
JAWS Stress (Item 2)	2.437	2.758	-2.264	77.270	0.026	0.132		0.306
Note. * p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across all 5 tests (corrected threshold: p < .01).

Note. Box plots display the median (centre line), interquartile range (IQR; box), and 1.5 times the IQR (whiskers). Dots represent outliers falling beyond the whiskers.

Plain text Summary. We compared wellbeing and work-related stress between Metro and Regional/Remote judicial officers. There were no meaningful differences in psychological distress, secondary traumatic stress, or life satisfaction between the two groups. However, Regional/Remote officers did report higher work-related stress than their Metro counterparts, and this difference was statistically significant

Summary w/Statistics.. We conducted independent samples t-tests to examine whether wellbeing outcomes and JAWS stress scores differed between Metro and Other (Regional, Remote) judicial officers across the full sample, with Bonferroni correction applied across all five tests (corrected threshold: p < .01).

No significant differences were found between Metro and Regional/Remote judicial officers on any of the three wellbeing measures after correction. Although Regional/Remote officers reported slightly higher psychological distress (K10; t(85.53) = -1.86, p = .067, d = 0.23) and secondary traumatic stress (STSS; t(81.13) = -1.59, p = .115, d = 0.22) than Metro officers, neither result survived Bonferroni correction. No difference was found for life satisfaction (SWLS; t(89.67) = 0.37, p = .714, d = 0.04).

For JAWS stress, Regional/Remote officers reported significantly higher scores on Item 1 (t(81.51) = -4.96, p < .001, d = 0.61) than Metro officers, a medium to large effect that remained significant after correction. Item 2 showed a similar pattern but did not survive correction (t(77.27) = -2.26, p = .026, p adjusted = .132, d = 0.31).

Limitations. As mentioned in above sections, running multiple tests increases the risk of Type I error. We applied Bonferroni correction to partially address this, but it does not completely remove the risk. Additionally, the Regional/Remote sample is considerably smaller than the Metro sample (n = 67 vs n = 512), which reduces statistical power and may explain why some differences did not reach significance after correction.

Multiple Regression Predicting Wellbeing Outcomes using Court Level, Gender, and region

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Multiple Regression: Court Level, Gender, and Region Predicting Wellbeing
Outcome	Predictor	B	SE	t	p	95% CI Lower	95% CI Upper
Psychological Distress (K10)	Intercept (Higher, Male, Metro)	16.268	0.533	30.511	0.000	15.220	17.315
	CourtLevel_fHigher	-1.244	0.573	-2.169	0.031	-2.370	-0.117
	Gender_fFemale	2.153	0.545	3.950	0.000	1.082	3.224
	Location_fRegional	0.987	0.876	1.127	0.260	-0.734	2.708
Secondary Traumatic Stress (STSS)	Intercept (Higher, Male, Metro)	31.477	1.032	30.487	0.000	29.449	33.506
	CourtLevel_fHigher	-1.958	1.114	-1.758	0.079	-4.147	0.230
	Gender_fFemale	4.013	1.057	3.796	0.000	1.936	6.090
	Location_fRegional	1.657	1.694	0.978	0.329	-1.672	4.986
Satisfaction With Life (SWLS)	Intercept (Higher, Male, Metro)	23.953	0.508	47.135	0.000	22.955	24.952
	CourtLevel_fHigher	1.439	0.548	2.627	0.009	0.363	2.515
	Gender_fFemale	-0.602	0.521	-1.156	0.248	-1.625	0.421
	Location_fRegional	0.173	0.837	0.206	0.837	-1.472	1.817

Model Fit Statistics: Court Level, Gender, and Region Predicting Wellbeing
Outcome	R2	Adj. R2	F	p	df	df error
Psychological Distress (K10)	0.048	0.043	8.688	0.000	3	512
Secondary Traumatic Stress (STSS)	0.042	0.036	7.353	0.000	3	507
Satisfaction With Life (SWLS)	0.018	0.012	3.116	0.026	3	512

Plain text Summary. We examined whether court level, gender, and location predicted wellbeing outcomes. Gender was the most consistent predictor; women reported significantly higher psychological distress and secondary traumatic stress than men. Additionally, local court officers reported higher distress and lower life satisfaction than Higher Court officers. Location was not a significant predictor once court level and gender were accounted for statistically. These three predictors together explained only a small proportion of wellbeing variance, which tells us that other factors not captured here might play a larger role.

Summary w/Statistics. We conducted three multiple regression analyses to examine whether court level, gender, and location independently predicted psychological distress (K10), satisfaction with life (SWLS), and secondary traumatic stress (STSS). The reference categories (i.e., the baseline group other groups are compared against in the regression) were Higher Court, Male, and Metro.

Psychological Distress (K10): The overall model was significant (F(3, 512) = 8.69, p < .001) and explained 4.8% of variance (R² = .048, adjusted R² = .043). Local court officers reported significantly higher distress than Higher Court officers (B = -1.24, p = .031), and women officers reported significantly higher distress than males (B = 2.15, p < .001). Location was not a significant predictor (B = 0.99, p = .260).

Secondary Traumatic Stress (STSS): The overall model was significant (F(3, 507) = 7.35, p < .001) and explained 4.2% of variance (R² = .042, adjusted R² = .036). Women officers reported significantly higher secondary traumatic stress than males (B = 4.01, p < .001). Court level (B = -1.96, p = .079) and location (B = 1.66, p = .329) were not significant predictors.

Satisfaction with Life (SWLS): The overall model was significant (F(3, 512) = 3.12, p = .026) but explained only 1.8% of variance (R² = .018, adjusted R² = .012). Higher Court officers reported significantly higher life satisfaction than Local Court officers (B = 1.44, p = .009). Neither gender (B = -0.60, p = .248) nor location (B = 0.17, p = .837) were significant predictors.

Limitations. Three separate regression models were estimated, one for each wellbeing outcome. While each model was theoretically motivated, running multiple models on the same sample does still increase the risk of stumbling across chance findings. Moreover, the models explain a relatively small proportion of variance, which tells us that other factors not captured here contribute substantially to judicial officer wellbeing.

Correlational Analyses for Wellbeing measures

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Pearson Correlations Between Wellbeing Measures
Measure	K10	STSS	SWLS
K10 (Psychological Distress)		0.84***	-0.49***
STSS (Secondary Traumatic Stress)	0.84***		-0.49***
SWLS (Satisfaction With Life)	-0.49***	-0.49***
Note. • p < .05, p < .01, * p < .001

Judicial Officer Coping Strategy Use Across Gender, Court Level, and Region

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Chi-Square/Fisher’s Exact Tests: Coping Strategies by Gender
Coping Strategy	Test	Chi-Square	df	p	p_adj	sig
Alcohol	Chi-Square	0.02	1	0.900	0.930
Smoking/Vaping	Chi-Square	3.25	1	0.071	0.143
Prescribed Meds	Chi-Square	2.70	1	0.101	0.176
Non-Prescribed Meds	Chi-Square	5.82	1	0.016	0.037
Prof. Mental Health Support	Chi-Square	2.06	1	0.152	0.236
Other	Chi-Square	0.01	1	0.930	0.930
None of the Above	Chi-Square	7.03	1	0.008	0.037
Prefer Not to Say	Fisher’s Exact	NA	NA	0.370	0.471
Support from Colleagues	Chi-Square	14.38	1	0.000	0.002	**
Support from Family & Friends	Chi-Square	12.25	1	0.000	0.003	**
Exercise	Chi-Square	0.04	1	0.850	0.930
Meditation/Mindfulness	Chi-Square	6.11	1	0.013	0.037
Faith Practices	Chi-Square	1.23	1	0.268	0.375
Artistic/Creative Activities	Chi-Square	6.43	1	0.011	0.037
Note. • p < .05, p < .01, * p < .001. Significance based on p_adj (FDR corrected, Benjamini-Hochberg, corrected threshold: p < .05). Fisher’s Exact Test used when expected cell count < 5. Males are the reference group.

Chi-Square/Fisher’s Exact Tests: Coping Strategies by Court Level
Coping Strategy	Test	Chi-Square	df	p	p_adj
Alcohol	Chi-Square	0.13	1	0.722	1.000
Smoking/Vaping	Chi-Square	3.41	1	0.065	0.452
Prescribed Meds	Chi-Square	0.22	1	0.642	0.999
Non-Prescribed Meds	Fisher’s Exact	NA	NA	1.000	1.000
Prof. Mental Health Support	Chi-Square	9.23	1	0.002	0.033
Other	Chi-Square	1.29	1	0.256	0.999
None of the Above	Chi-Square	0.07	1	0.786	1.000
Prefer Not to Say	Fisher’s Exact	NA	NA	0.622	0.999
Support from Colleagues	Chi-Square	0.64	1	0.425	0.999
Support from Family & Friends	Chi-Square	0.45	1	0.503	0.999
Exercise	Chi-Square	0.55	1	0.460	0.999
Meditation/Mindfulness	Chi-Square	0.48	1	0.488	0.999
Faith Practices	Chi-Square	0.00	1	0.968	1.000
Artistic/Creative Activities	Chi-Square	0.00	1	0.965	1.000
Note. • p < .05, p < .01, * p < .001. Fisher’s Exact Test used when expected cell count < 5. Higher Court is the reference group. p_adj reflects FDR correction (Benjamini-Hochberg) across 14 tests.

Chi-Square/Fisher’s Exact Tests: Coping Strategies by Region
Coping Strategy	Test	Chi-Square	df	p	p_adj
Alcohol	Chi-Square	7.97	1	0.005	0.067
Smoking/Vaping	Fisher’s Exact	NA	NA	0.357	0.785
Prescribed Meds	Chi-Square	0.73	1	0.393	0.785
Non-Prescribed Meds	Fisher’s Exact	NA	NA	0.357	0.785
Prof. Mental Health Support	Chi-Square	0.07	1	0.789	0.917
Other	Chi-Square	0.03	1	0.852	0.917
None of the Above	Fisher’s Exact	NA	NA	0.379	0.785
Prefer Not to Say	Fisher’s Exact	NA	NA	1.000	1.000
Support from Colleagues	Chi-Square	0.81	1	0.368	0.785
Support from Family & Friends	Chi-Square	0.16	1	0.687	0.917
Exercise	Chi-Square	0.18	1	0.674	0.917
Meditation/Mindfulness	Chi-Square	1.80	1	0.180	0.785
Faith Practices	Chi-Square	0.11	1	0.739	0.917
Artistic/Creative Activities	Chi-Square	0.09	1	0.770	0.917
Note. • p < .05, p < .01, * p < .001. Fisher’s Exact Test used when expected cell count < 5. Metro is the reference group. p_adj reflects FDR correction (Benjamini-Hochberg) across 14 tests.

Plain text Summary. We examined whether coping strategy use endorsed by the judicial officers differed by gender, court level, and region. Gender showed the most variation, with six significant differences found. Women were more likely to seek support from colleagues and family and friends, less likely to report using no coping strategies, and more likely to use meditation/mindfulness, artistic/creative activities, and non-prescribed medications than men. Local court officers were more likely to seek professional mental health support than Higher Court officers. No significant differences were found by region after correction.

Summary w/Statistics. We conducted chi-square tests (or Fisher’s Exact Tests where expected cell counts were below 5) to examine whether coping strategy use differed by gender, court level, and region. FDR correction (Benjamini-Hochberg) was applied across 14 tests within each comparison.

By Gender: After FDR correction, six significant differences were found. Women were significantly more likely to seek support from colleagues (χ²(1) = 14.38, p < .001, p_adj = .002) and family and friends (χ²(1) = 12.25, p < .001, p_adj = .003) than men. Women were also significantly less likely to report using none of the listed coping strategies (χ²(1) = 7.03, p = .008, p_adj = .037), suggesting they tend to use more coping strategies overall, and more likely to engage in meditation/mindfulness (χ²(1) = 6.11, p = .013, p_adj = .037), artistic/creative activities (χ²(1) = 6.43, p = .011, p_adj = .037), and non-prescribed medications (χ²(1) = 5.82, p = .016, p_adj = .037) than men. No significant differences were found for alcohol, smoking/vaping, prescribed medications, professional mental health support, exercise, or faith practices.

By Court Level: One significant difference remained after FDR correction. Local court officers were significantly more likely to seek professional mental health support than Higher Court officers (χ²(1) = 9.23, p = .002, p_adj = .033). No other coping strategies differed significantly by court level.

By Region: No significant differences were found after FDR correction. Although alcohol use showed a nominally significant difference before correction (χ²(1) = 7.97, p = .005), this did not survive FDR adjustment (p_adj = .067).

Limitations. Fourteen chi-square or Fisher’s Exact tests were conducted for each demographic comparison, totalling 42 tests across the three tables. FDR correction (Benjamini-Hochberg) was applied within each comparison group to control the false discovery rate. While this is less conservative than Bonferroni, results should still be interpreted with caution given the exploratory nature of the analysis.

Association between Judicial Officer Coping Strategy Use and wellbeing measures

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Coping Strategy Use and Psychological Distress (K10)
Coping Strategy	Mean (No)	Mean (Yes)	t	df	p	p (adjusted)	sig
Alcohol	16.58	17.40	-1.52	423.9	0.128	1.000
Smoking/Vaping	16.74	23.45	-3.21	10.3	0.009	0.126
Prescribed Meds	16.19	22.08	-5.84	72.8	0.000	0.000	***
Non-Prescribed Meds	16.74	23.55	-3.05	10.3	0.012	0.166
Prof. Mental Health Support	16.01	20.58	-6.06	135.4	0.000	0.000	***
Other	16.89	16.62	0.32	67.4	0.747	1.000
None of the Above	16.94	13.69	2.23	12.8	0.044	0.621
Prefer Not to Say	16.85	19.50	-2.17	3.3	0.110	1.000
Support from Colleagues	17.70	16.61	1.58	186.2	0.116	1.000
Support from Family & Friends	16.09	17.00	-1.22	113.7	0.227	1.000
Exercise	17.42	16.73	0.98	157.6	0.329	1.000
Meditation/Mindfulness	16.42	18.29	-2.94	210.3	0.004	0.051
Faith Practices	16.88	16.73	0.15	56.4	0.883	1.000
Artistic/Creative Activities	16.72	17.46	-1.16	177.6	0.246	1.000
Note. • p < .05, p < .01, * p < .001. Significance based on Bonferroni corrected p values (corrected threshold: p < .004). n comparisons = 14. Mean (No) = did not endorse strategy, Mean (Yes) = endorsed strategy.

Coping Strategy Use and Secondary Traumatic Stress (STSS)
Coping Strategy	Mean (No)	Mean (Yes)	t	df	p	p (adjusted)	sig
Alcohol	32.13	34.15	-1.91	428.5	0.056	0.791
Smoking/Vaping	32.75	37.45	-1.37	10.5	0.198	1.000
Prescribed Meds	31.77	41.09	-5.17	75.8	0.000	0.000	***
Non-Prescribed Meds	32.61	45.60	-3.36	9.3	0.008	0.112
Prof. Mental Health Support	31.26	39.69	-6.02	142.0	0.000	0.000	***
Other	32.83	32.98	-0.09	65.9	0.931	1.000
None of the Above	32.98	26.75	1.78	11.5	0.101	1.000
Prefer Not to Say	32.87	29.50	0.70	3.1	0.532	1.000
Support from Colleagues	34.49	32.36	1.57	181.6	0.119	1.000
Support from Family & Friends	31.01	33.16	-1.47	111.3	0.144	1.000
Exercise	33.83	32.61	0.87	150.0	0.383	1.000
Meditation/Mindfulness	32.02	35.47	-2.81	214.3	0.005	0.076
Faith Practices	32.90	32.23	0.35	55.2	0.727	1.000
Artistic/Creative Activities	32.33	34.92	-1.95	163.5	0.053	0.741
Note. • p < .05, p < .01, * p < .001. Significance based on Bonferroni corrected p values (corrected threshold: p < .004). n comparisons = 14. Mean (No) = did not endorse strategy, Mean (Yes) = endorsed strategy.

Coping Strategy Use and Satisfaction With Life (SWLS)
Coping Strategy	Mean (No)	Mean (Yes)	t	df	p	p (adjusted)	sig
Alcohol	24.53	24.16	0.74	442.0	0.459	1.000
Smoking/Vaping	24.49	19.82	2.15	10.3	0.057	0.791
Prescribed Meds	24.67	22.35	2.64	76.3	0.010	0.142
Non-Prescribed Meds	24.48	20.45	1.86	10.3	0.092	1.000
Prof. Mental Health Support	24.94	22.00	4.43	144.0	0.000	0.000	***
Other	24.29	25.48	-1.63	69.7	0.107	1.000
None of the Above	24.43	23.08	0.83	11.5	0.422	1.000
Prefer Not to Say	24.50	10.50	7.51	3.1	0.004	0.061
Support from Colleagues	22.67	24.92	-3.46	183.4	0.001	0.009	**
Support from Family & Friends	22.08	24.81	-3.47	104.7	0.001	0.010	•
Exercise	22.82	24.78	-2.90	150.4	0.004	0.061
Meditation/Mindfulness	24.34	24.61	-0.49	244.2	0.622	1.000
Faith Practices	24.46	23.73	0.77	55.7	0.447	1.000
Artistic/Creative Activities	24.37	24.52	-0.23	170.0	0.816	1.000
Note. • p < .05, p < .01, * p < .001. Significance based on Bonferroni corrected p values (corrected threshold: p < .004). n comparisons = 14. Mean (No) = did not endorse strategy, Mean (Yes) = endorsed strategy.

Plain text Summary. We examined whether using each coping strategy was associated with higher or lower psychological distress, life satisfaction, and secondary traumatic stress. Prescribed medication use and professional mental health support were consistently associated with higher distress and secondary trauma. Professional mental health support was also associated with lower life satisfaction. Support from colleagues and family and friends showed a similar pattern but did not survive correction.

Summary w/Statistics. We conducted independent samples t-tests to examine whether judicial officers who endorsed each coping strategy reported different levels of psychological distress (K10), life satisfaction (SWLS), and secondary traumatic stress (STSS) compared to those who did not. Bonferroni correction was applied across 14 tests within each outcome (corrected threshold: p < .004).

Psychological Distress (K10): Two coping strategies were significantly associated with higher K10 scores after correction. Officers who used prescribed medications reported significantly higher distress than those who did not (M = 22.08 vs 16.19; t(72.8) = -5.84, p < .001). Officers who sought professional mental health support also reported significantly higher distress (M = 20.58 vs 16.01; t(135.4) = -6.06, p < .001). No other strategies were significantly associated with K10 after correction.

Secondary Traumatic Stress (STSS): Two strategies were significantly associated with higher STSS after correction. Officers using prescribed medications reported significantly higher secondary traumatic stress than those who did not (M = 41.09 vs 31.77; t(75.8) = -5.17, p < .001), as did those seeking professional mental health support (M = 39.69 vs 31.26; t(142.0) = -6.02, p < .001). No other strategies were significantly associated with STSS after correction.

Satisfaction with Life (SWLS): One strategy was significantly associated with SWLS after correction. Officers who sought professional mental health support reported significantly lower life satisfaction than those who did not (M = 22.00 vs 24.94; t(144.0) = 4.43, p < .001). Although support from colleagues (M = 24.92 vs 22.67; t(183.4) = -3.46, p_adj = .009) and family and friends (M = 24.81 vs 22.08; t(104.7) = -3.47, p_adj = .010) were associated with higher life satisfaction before correction, neither survived the Bonferroni corrected threshold of p < .004. No other strategies were significantly associated with SWLS after correction.

Limitations. These analyses are cross-sectional, so we cannot determine the direction of the relationship between coping strategy use and wellbeing. Additionally, Bonferroni correction was applied to account for the 14 t-tests conducted per outcome, but the risk of Type I error cannot be completely eliminated.

Household and Labour Dynamics across Gender, Court Level, and Region

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Gender Differences in HILDA Work-Life Balance Items
HILDA Item	Mean (Male, n=302)	SD (Male)	Mean (Female, n=291)	SD (Female)	t	df	p	p (adjusted)	sig
Job interferes with family/home	2.95	0.90	2.63	0.89	4.22	562.8	0.000	0.000	***
Family/home interferes with job	3.66	0.80	3.54	0.80	1.76	563.0	0.078	0.234
Sense of time	2.09	0.64	1.67	0.60	7.93	561.1	0.000	0.000	***
Note. • p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 3 tests (corrected threshold: p < .017). Higher scores indicate less frequency (1 = Always, 5 = Never). Can’t Choose responses excluded.

Regional Differences in HILDA Work-Life Balance Items
HILDA Item	Mean (Metro, n=512)	SD (Metro)	Mean (Regional, n=67)	SD (Regional)	t	df	p	p (adjusted)
Job interferes with family/home	2.80	0.90	2.63	1.00	1.25	74.3	0.215	0.645
Family/home interferes with job	3.58	0.81	3.63	0.66	-0.49	86.4	0.623	1.000
Sense of time	1.88	0.66	1.90	0.67	-0.31	76.6	0.755	1.000
Note. • p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 3 tests (corrected threshold: p < .017). Higher scores indicate less frequency (1 = Always, 5 = Never). Can’t Choose responses excluded.

Court Level Differences in HILDA Work-Life Balance Items
HILDA Item	Mean (Sup.)	SD (Sup.)	Mean (Dist.)	SD (Dist.)	Mean (Loc.)	SD (Loc.)	F	df	p	p (adjusted)
Job interferes with family/home	2.66	0.91	2.75	0.84	2.89	0.95	2.90	2	0.056	0.168
Family/home interferes with job	3.43	0.80	3.60	0.84	3.67	0.76	3.73	2	0.025	0.075
Sense of time	1.89	0.65	1.89	0.63	1.87	0.68	0.03	2	0.970	1.000
Note. • p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 3 tests (corrected threshold: p < .017). Higher scores indicate less frequency (1 = Always, 5 = Never). Can’t Choose responses excluded.

Plain text Summary. We examined work-life balance across gender, location, and court level using three HILDA items. Women reported more frequent work-life interference than men, particularly feeling that their job interferes with family life and feeling more rushed for time overall. No significant differences were found by location or court level after correction.

Summary w/Statistics. We examined gender, location, and court level differences across three HILDA work-life balance items using independent samples t-tests and one-way ANOVAs respectively. Bonferroni correction was applied across all 3 tests within each comparison (corrected threshold: p < .017). Note that higher scores indicate less frequency of interference (1 = Always, 5 = Never), so lower mean scores reflect more frequent work-life conflict.

Gender Differences: Two significant differences were found after Bonferroni correction. Women reported more frequent job interference with family and home life than men (Women M = 2.63, Men M = 2.95; t(562.8) = 4.22, p < .001, p_adj < .001). Women also reported feeling more rushed for time than men (Women M = 1.67, Men M = 2.09; t(561.1) = 7.93, p < .001, p_adj < .001). Note that for both items, lower scores indicate more frequent interference or feeling more rushed, so women’s lower means reflect greater work-life conflict and time pressure. No significant difference was found for family or home interfering with job (t(563.0) = 1.76, p = .078, p_adj = .234).

Location Differences: No significant differences were found between Metro and Regional/Remote officers on any of the three work-life balance items after correction. Job interference with family (t(74.3) = 1.25, p = .215, p_adj = .645), family interference with job (t(86.4) = -0.49, p = .623, p_adj = 1.000), and sense of time (t(76.6) = -0.31, p = .755, p_adj = 1.000) were all comparable between groups.

Court Level Differences: No significant differences were found across Supreme, District, and Local court levels after correction. Family or home interfering with job showed a nominally significant difference before correction (F(2) = 3.73, p = .025) but did not survive Bonferroni correction (p_adj = .075), with Local court officers reporting slightly more frequent interference (M = 3.67) than Supreme Court officers (M = 3.43). Job interference with family (F(2) = 2.90, p = .056, p_adj = .168) and sense of time (F(2) = 0.03, p = .970, p_adj = 1.000) were not significant.

Limitations. Bonferroni correction was applied across 3 tests per comparison to reduce the risk of Type I error.

Psychosocial Safety Climate across Gender, Court Level, and Region

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PSCS Average by Gender
Group	n	Mean	SD	Min	Max
Female	282	3.00	0.98	1	5
Male	278	3.33	0.95	1	5
Note. Higher scores indicate higher psychosocial safety climate.

PSCS Average by Court Level
Group	n	Mean	SD	Min	Max
Supreme	110	3.56	0.78	1	5
District	148	3.07	1.00	1	5
Local	256	3.05	0.99	1	5
Note. Higher scores indicate higher psychosocial safety climate.

PSCS Average by Region
Group	n	Mean	SD	Min	Max
Metro	483	3.17	0.98	1	5.00
Regional	62	3.12	0.94	1	4.75
Note. Higher scores indicate higher psychosocial safety climate.

Sharyn: JAWS Response Patterns by Gender

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Table 1. JAWS Item Frequencies: Source of Satisfaction and Stress
	Source of Satisfaction							Source of Stress
Item	Sat n	Never/Almost Never	Seldom	Sometimes	Often	Always/Almost Always	N/A	Str n	Never/Almost Never	Seldom	Sometimes	Often	Always/Almost Always	N/A
Working with judicial colleagues	571	8 (1.4%)	30 (5.3%)	119 (20.8%)	247 (43.3%)	167 (29.2%)	0 (0%)	571	132 (23.1%)	213 (37.3%)	171 (29.9%)	47 (8.2%)	8 (1.4%)	0 (0%)
Working with other court staff	574	5 (0.9%)	19 (3.3%)	119 (20.7%)	272 (47.4%)	159 (27.7%)	0 (0%)	574	140 (24.4%)	225 (39.2%)	165 (28.7%)	35 (6.1%)	9 (1.6%)	0 (0%)
Dealing with other staff in court	573	4 (0.7%)	35 (6.1%)	158 (27.6%)	262 (45.7%)	114 (19.9%)	0 (0%)	572	118 (20.6%)	239 (41.8%)	155 (27.1%)	48 (8.4%)	12 (2.1%)	0 (0%)
Relationship between judiciary and court admin	568	26 (4.6%)	100 (17.6%)	177 (31.2%)	166 (29.2%)	99 (17.4%)	0 (0%)	569	95 (16.7%)	168 (29.5%)	194 (34.1%)	90 (15.8%)	22 (3.9%)	0 (0%)
Appellate review of decisions	556	66 (11.9%)	142 (25.5%)	251 (45.1%)	77 (13.8%)	20 (3.6%)	0 (0%)	563	71 (12.6%)	134 (23.8%)	217 (38.5%)	87 (15.5%)	54 (9.6%)	0 (0%)
Judicial leadership within my court	565	55 (9.7%)	101 (17.9%)	159 (28.1%)	139 (24.6%)	111 (19.6%)	0 (0%)	565	109 (19.3%)	163 (28.8%)	156 (27.6%)	92 (16.3%)	45 (8%)	0 (0%)
Media commentary	547	157 (28.7%)	203 (37.1%)	167 (30.5%)	17 (3.1%)	3 (0.5%)	0 (0%)	553	79 (14.3%)	156 (28.2%)	208 (37.6%)	80 (14.5%)	30 (5.4%)	0 (0%)
Capacity to develop professionally	574	41 (7.1%)	98 (17.1%)	197 (34.3%)	168 (29.3%)	70 (12.2%)	0 (0%)	572	130 (22.7%)	197 (34.4%)	168 (29.4%)	61 (10.7%)	16 (2.8%)	0 (0%)
Going on circuit	423	25 (5.9%)	56 (13.2%)	113 (26.7%)	119 (28.1%)	110 (26%)	0 (0%)	427	98 (23%)	138 (32.3%)	122 (28.6%)	55 (12.9%)	14 (3.3%)	0 (0%)
Living away from home	383	89 (23.2%)	97 (25.3%)	122 (31.9%)	54 (14.1%)	21 (5.5%)	0 (0%)	385	77 (20%)	95 (24.7%)	117 (30.4%)	63 (16.4%)	33 (8.6%)	0 (0%)

Table 2. Often and Always/Almost Always N(%) by Gender
	Satisfaction						Stress
	Male			Female			Male			Female
Item	n	Often	Always	n	Often	Always	n	Often	Always	n	Often	Always
Working with judicial colleagues	302	113 (40.6%)	88 (31.7%)	291	127 (44.7%)	78 (27.5%)	302	27 (9.7%)	2 (0.7%)	291	20 (7%)	5 (1.8%)
Working with other court staff	302	128 (45.6%)	83 (29.5%)	291	141 (49.6%)	74 (26.1%)	302	13 (4.6%)	2 (0.7%)	291	20 (7%)	6 (2.1%)
Dealing with other staff in court	302	121 (43.2%)	60 (21.4%)	291	139 (48.9%)	53 (18.7%)	302	20 (7.2%)	3 (1.1%)	291	26 (9.2%)	8 (2.8%)
Relationship between judiciary and court admin	302	82 (29.4%)	56 (20.1%)	291	80 (28.6%)	43 (15.4%)	302	31 (11.1%)	5 (1.8%)	291	57 (20.4%)	16 (5.7%)
Appellate review of decisions	302	35 (12.8%)	9 (3.3%)	291	41 (15%)	10 (3.7%)	302	36 (12.9%)	15 (5.4%)	291	49 (17.8%)	39 (14.1%)
Judicial leadership within my court	302	68 (24.7%)	60 (21.8%)	291	71 (25.3%)	50 (17.8%)	302	35 (12.7%)	19 (6.9%)	291	54 (19.2%)	26 (9.3%)
Media commentary	302	10 (3.8%)	2 (0.8%)	291	7 (2.6%)	1 (0.4%)	302	32 (11.8%)	8 (3%)	291	45 (16.4%)	21 (7.7%)
Capacity to develop professionally	302	87 (31.1%)	33 (11.8%)	291	78 (27.4%)	37 (13%)	302	19 (6.8%)	7 (2.5%)	291	40 (14%)	9 (3.2%)
Going on circuit	302	56 (26.3%)	60 (28.2%)	291	62 (30.5%)	49 (24.1%)	302	20 (9.3%)	1 (0.5%)	291	35 (17%)	12 (5.8%)
Living away from home	302	32 (16.3%)	12 (6.1%)	291	22 (12.2%)	9 (5%)	302	32 (16.1%)	7 (3.5%)	291	30 (16.8%)	24 (13.4%)

Table 3. Often and Always/Almost Always N(%) by Court Level
	Satisfaction									Stress
	Supreme			District			Local			Supreme			District			Local
Item	n	Often	Always	n	Often	Always	n	Often	Always	n	Often	Always	n	Often	Always	n	Often	Always
Working with judicial colleagues	122	50 (43.9%)	46 (40.4%)	157	56 (38.1%)	41 (27.9%)	268	113 (44.1%)	68 (26.6%)	122	5 (4.4%)	1 (0.9%)	157	12 (8.2%)	0 (0%)	268	26 (10.2%)	6 (2.3%)
Working with other court staff	122	57 (49.6%)	37 (32.2%)	157	67 (45%)	37 (24.8%)	268	127 (49.4%)	74 (28.8%)	122	3 (2.6%)	0 (0%)	157	10 (6.7%)	1 (0.7%)	268	17 (6.6%)	6 (2.3%)
Dealing with other staff in court	122	52 (46%)	22 (19.5%)	157	61 (40.9%)	26 (17.4%)	268	127 (49.2%)	55 (21.3%)	122	8 (7.1%)	0 (0%)	157	13 (8.7%)	2 (1.3%)	268	23 (8.9%)	6 (2.3%)
Relationship between judiciary and court admin	122	34 (30.6%)	15 (13.5%)	157	38 (26.2%)	19 (13.1%)	268	80 (31%)	56 (21.7%)	122	13 (11.6%)	2 (1.8%)	157	29 (20%)	7 (4.8%)	268	40 (15.5%)	10 (3.9%)
Appellate review of decisions	122	16 (14.7%)	6 (5.5%)	157	20 (13.5%)	6 (4.1%)	268	34 (13.7%)	7 (2.8%)	122	8 (7.2%)	7 (6.3%)	157	36 (24.3%)	14 (9.5%)	268	36 (14.2%)	27 (10.7%)
Judicial leadership within my court	122	35 (31.2%)	36 (32.1%)	157	39 (26.4%)	26 (17.6%)	268	53 (21%)	38 (15.1%)	122	8 (7.1%)	1 (0.9%)	157	23 (15.5%)	9 (6.1%)	268	50 (19.9%)	28 (11.2%)
Media commentary	122	3 (2.8%)	0 (0%)	157	2 (1.4%)	1 (0.7%)	268	9 (3.7%)	2 (0.8%)	122	11 (10%)	3 (2.7%)	157	22 (15.3%)	7 (4.9%)	268	41 (16.5%)	15 (6%)
Capacity to develop professionally	122	47 (41.2%)	18 (15.8%)	157	41 (27.7%)	21 (14.2%)	268	64 (24.8%)	28 (10.9%)	122	7 (6.1%)	2 (1.8%)	157	18 (12.2%)	2 (1.4%)	268	30 (11.7%)	10 (3.9%)
Going on circuit	122	10 (16.7%)	13 (21.7%)	157	46 (36.2%)	34 (26.8%)	268	56 (28.6%)	49 (25%)	122	2 (3.3%)	1 (1.7%)	157	16 (12.5%)	6 (4.7%)	268	33 (16.7%)	7 (3.5%)
Living away from home	122	8 (13.3%)	3 (5%)	157	19 (16.1%)	8 (6.8%)	268	26 (15%)	8 (4.6%)	122	4 (6.7%)	2 (3.3%)	157	14 (11.9%)	7 (5.9%)	268	38 (21.8%)	23 (13.2%)

Natalie: JAWS Court Leadership as Source of Satisfaction and Stress

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Judicial Leadership Within My Court: Source of Satisfaction and Stress
Category	Satisfaction	Stress
Never/Almost Never	55 (9.7%)	109 (19.3%)
Seldom	101 (17.9%)	163 (28.8%)
Sometimes	159 (28.1%)	156 (27.6%)
Often	139 (24.6%)	92 (16.3%)
Always/Almost Always	111 (19.6%)	45 (8%)
N/A	0 (0%)	0 (0%)

Judicial Leadership Within My Court by Gender
	Male		Female
Category	Satisfaction	Stress	Satisfaction	Stress
Never/Almost Never	29 (10.5%)	68 (24.7%)	26 (9.3%)	41 (14.6%)
Seldom	37 (13.5%)	88 (32%)	61 (21.7%)	73 (26%)
Sometimes	81 (29.5%)	65 (23.6%)	73 (26%)	87 (31%)
Often	68 (24.7%)	35 (12.7%)	71 (25.3%)	54 (19.2%)
Always/Almost Always	60 (21.8%)	19 (6.9%)	50 (17.8%)	26 (9.3%)
N/A	0 (0%)	0 (0%)	0 (0%)	0 (0%)

Judicial Leadership Within My Court by Court Level
	Supreme		District		Local
Category	Satisfaction	Stress	Satisfaction	Stress	Satisfaction	Stress
Never/Almost Never	5 (4.5%)	37 (33%)	15 (10.1%)	30 (20.3%)	28 (11.1%)	34 (13.5%)
Seldom	5 (4.5%)	32 (28.6%)	35 (23.6%)	44 (29.7%)	55 (21.8%)	75 (29.9%)
Sometimes	31 (27.7%)	34 (30.4%)	33 (22.3%)	42 (28.4%)	78 (31%)	64 (25.5%)
Often	35 (31.2%)	8 (7.1%)	39 (26.4%)	23 (15.5%)	53 (21%)	50 (19.9%)
Always/Almost Always	36 (32.1%)	1 (0.9%)	26 (17.6%)	9 (6.1%)	38 (15.1%)	28 (11.2%)
N/A	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)

Judicial Leadership Within My Court by Location
	Metro		Other (Regional, Remote)
Category	Satisfaction	Stress	Satisfaction	Stress
Never/Almost Never	46 (9.5%)	98 (20.3%)	6 (10%)	6 (10%)
Seldom	84 (17.4%)	136 (28.2%)	13 (21.7%)	22 (36.7%)
Sometimes	133 (27.6%)	133 (27.5%)	18 (30%)	16 (26.7%)
Often	123 (25.5%)	78 (16.1%)	12 (20%)	10 (16.7%)
Always/Almost Always	96 (19.9%)	38 (7.9%)	11 (18.3%)	6 (10%)
N/A	0 (0%)	0 (0%)	0 (0%)	0 (0%)

Terese: Free Text Response Count by Gender

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Number of Participants Responding to Open-Ended Questions by Gender
Gender	STSS Open Text	Trauma Comments	K10 Open Text	JAWS Open Text	Final Comments	Manage Stress Other
Female	74	0	55	91	90	28
Male	66	1	43	82	70	25
NA	2	0	2	3	3	1
Total	142	1	100	176	163	54

Kylie: JAWS additional items and Queensland Local Court Wellbeing Outcomes by Gender

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Top 10 Most Stressful JAWS Items: Local Court (N = 268)
Rank	Item	Overall	Male	Female
1	Graphic audiovisual evidence	-1.17	-0.90	-1.33
2	Self-represented litigants	-1.15	-1.02	-1.22
3	AG public support	-0.95	-0.92	-0.94
4	Frequency of interruptions	-0.91	-0.67	-1.09
5	Rehabilitation programs	-0.87	-0.57	-1.09
6	Adequacy of staffing	-0.79	-0.27	-1.21
7	Media commentary	-0.71	-0.44	-0.87
8	Isolation of judicial role	-0.65	-0.38	-0.83
9	Financial benefits	-0.61	-0.67	-0.53
10	Workload	-0.60	-0.27	-0.89

Top 10 Most Stressful JAWS Items: QLD Local Court (N = 47)
Rank	Item	Overall	Male	Female
1	Rehabilitation programs	-1.57	-0.94	-1.96
2	Self-represented litigants	-1.52	-1.50	-1.56
3	Living away from home	-1.44	-0.50	-1.61
4	Adequacy of staffing	-1.40	-1.56	-1.24
5	AG public support	-0.98	-1.50	-0.62
6	Frequency of interruptions	-0.97	-1.40	-0.74
7	Isolation of judicial role	-0.74	-0.81	-0.68
8	Media commentary	-0.71	-0.56	-0.79
9	IT resources	-0.60	-0.69	-0.44
10	Pace of colleagues	-0.57	-0.64	-0.55

Top 10 Most Satisfying JAWS Items: Local Court (N = 268)
Rank	Item	Overall	Male	Female
1	Working with court staff	1.76	1.93	1.67
2	Remuneration	1.67	1.39	1.91
3	Camaraderie	1.66	1.69	1.63
4	Positive contribution to society	1.56	1.63	1.54
5	Dealing with other staff	1.49	1.73	1.38
6	Working with colleagues	1.45	1.58	1.36
7	Working with values	1.38	1.48	1.32
8	Formal judicial education	1.37	1.48	1.30
9	Privilege and status	1.08	1.29	0.92
10	Going on circuit	1.05	1.45	0.75

Top 10 Most Satisfying JAWS Items: QLD Local Court (N = 47)
Rank	Item	Overall	Male	Female
1	Remuneration	2.26	1.25	2.88
2	Dealing with other staff	1.26	1.44	1.20
3	Working with court staff	1.24	1.62	1.00
4	Leave entitlements	1.15	0.44	1.67
5	Working with values	1.12	0.88	1.36
6	Privilege and status	0.88	0.81	0.96
7	Professional activities outside court	0.88	1.09	0.80
8	Camaraderie	0.88	1.00	0.84
9	Positive contribution to society	0.83	0.88	0.84
10	Sentencing	0.69	0.81	0.60

K10, SWLS, & STSS Scores by Gender: QLD Local Court
Scale	Group	N	Mean	SD
K10	Female	26	16.23	3.76
	Male	20	19.85	6.71
	Total	47	17.87	5.45
SWLS	Female	26	25.46	5.68
	Male	20	21.70	6.30
	Total	47	23.62	6.28
STSS	Female	26	32.38	8.42
	Male	20	41.10	14.56
	Total	47	36.43	12.16

Wellbeing by Location: QLD Judicial Officers
Scale	Metro (n=88)	Regional (n=16)	t	df	p	p (adjusted)	sig	Cohen’s d
Psychological Distress (K10)	15.471	19.938	-3.094	20.469	0.006	0.017		0.863
Secondary Traumatic Stress (STSS)	30.349	43.188	-3.505	18.876	0.002	0.007	**	1.100
Satisfaction with Life (SWLS)	25.091	24.938	0.132	31.154	0.896	1.000		0.026
Note. * p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 3 tests (corrected threshold: p < .017).

Distribution of Metro vs Regional Location by Gender: QLD Judicial Officers
Gender	Metro	Regional
Male	53 (89.8%)	6 (10.2%)
Female	34 (77.3%)	10 (22.7%)

Chi-square test: Gender x Location X² = 2.148 , df = 1 , p = 0.143

Kylie II: Top Stressors and Satisfiers among Qld Judical officer

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Top 5 Most Commonly Nominated Stressors (Top 3): QLD Local Court (N = 47)
Item	N	Percent
Control over working day	4	8.5
Isolation of judicial role	4	8.5
Self-represented litigants	3	6.4
Adequacy of staffing	2	4.3
Adequacy of breaks	2	4.3

Top 5 Most Commonly Nominated Satisfiers (Top 3): QLD Local Court (N = 47)
Item	N	Percent
Assessing legal argument	4	8.5
Privilege and status	3	6.4
Leave entitlements	3	6.4
Remuneration	3	6.4
Physical court environment	2	4.3

JAWS Satisfaction and Stress Mean Scores: Metro vs Regional/Remote
Scale	Metro (n=512)	Regional (n=67)	t	df	p	p (adjusted)	sig	Cohen’s d
JAWS Satisfaction (Mean)	3.169	3.082	1.375	85.256	0.173	0.346		0.160
JAWS Stress (Mean)	2.696	2.967	-3.775	82.439	0.000	0.001	***	0.459
Note. * p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 2 tests (corrected threshold: p < .025).

Wellbeing Measures in the Lower Court by Gender

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Independent Samples T-Tests: K10, STSS, and SWLS by Gender — QLD Local Court
Scale	Mean Female	Mean Male	t	df	p
K10	19.85	16.23	2.16	28.0	0.039
STSS	41.10	32.38	2.39	28.6	0.024
SWLS	21.70	25.46	-2.09	38.7	0.043

Independent Samples T-Tests: K10, STSS, and SWLS by Gender — All QLD Judicial Officers
Scale	Mean Female	Mean Male	t	df	p
K10	15.98	16.47	-0.47	103.8	0.637
STSS	31.95	33.35	-0.58	101.5	0.566
SWLS	25.18	25.48	-0.26	94.1	0.793

NSW Judicial Officers: Wellbeing measures by Gender and Location

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NSW total N: 164 NSW Local Court N: 69

NSW total N: 164

Gender Differences in Wellbeing: All NSW Judicial Officers (N = 164)
Scale	Mean Male	Mean Female	t	df	p	p (adjusted)	sig	Cohen’s d
Psychological Distress (K10)	15.500	18.425	-2.706	137.752	0.008	0.023	•	0.438
Secondary Traumatic Stress (STSS)	29.871	35.917	-2.980	143.531	0.003	0.010	•	0.482
Satisfaction with Life (SWLS)	24.151	23.595	0.573	152.416	0.568	1.000		0.091
Note. • p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 3 tests (corrected threshold: p < .017).

Gender Distribution by Court Level: NSW Judicial Officers (N = 164)
Court Level	Male	Female
Supreme	16 (61.5%)	10 (38.5%)
District	27 (58.7%)	19 (41.3%)
Local	33 (48.5%)	35 (51.5%)

Chi-square test: Gender x Court Level (NSW) X² = 1.82 , df = 2 , p = 0.403

NSW Metro N (all courts): 124 NSW Regional N (all courts): 28

Wellbeing by Location: All NSW Judicial Officers
Scale	Metro (n=124)	Regional (n=28)	t	df	p	p (adjusted)	Cohen’s d
Psychological Distress (K10)	16.975	17.185	-0.169	48.122	0.867	1	0.03
Secondary Traumatic Stress (STSS)	32.892	33.148	-0.111	49.395	0.912	1	0.02
Satisfaction with Life (SWLS)	23.820	23.571	0.207	44.377	0.837	1	0.04
Note. • p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 3 tests (corrected threshold: p < .017).

Distribution of Metro vs Regional Location by Gender: All NSW Judicial Officers
Gender	Metro	Regional
Male	68 (86.1%)	11 (13.9%)
Female	55 (76.4%)	17 (23.6%)

Chi-square test: Gender x Location (All NSW) X² = 1.743 , df = 1 , p = 0.187

NSW Local Court N: 69

Gender Differences in Wellbeing: NSW Local Court (N = 69)
Scale	Mean Male	Mean Female	t	df	p	p (adjusted)	Cohen’s d
Psychological Distress (K10)	16.34	19.09	-1.69	62.4	0.097	0.290	0.413
Satisfaction with Life (SWLS)	23.06	22.94	0.08	65.8	0.939	1.000	0.019
Secondary Traumatic Stress (STSS)	31.12	36.09	-1.66	64.9	0.101	0.304	0.405
Note. • p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 3 tests (corrected threshold: p < .017).

NSW Local Court Metro N: 40 NSW Local Court Regional N: 21

Wellbeing by Location: NSW Local Court Judicial Officers
Scale	Metro (n=40)	Regional (n=21)	t	df	p	p (adjusted)	Cohen’s d
Psychological Distress (K10)	18.842	17.150	1.004	52.285	0.320	0.96	0.247
Secondary Traumatic Stress (STSS)	34.256	34.190	0.021	48.464	0.984	1.00	0.005
Satisfaction with Life (SWLS)	22.950	22.952	-0.001	45.509	0.999	1.00	0.000
Note. • p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 3 tests (corrected threshold: p < .017).

Distribution of Metro vs Regional Location by Gender: NSW Local Court
Gender	Metro	Regional
Male	19 (67.9%)	9 (32.1%)
Female	20 (62.5%)	12 (37.5%)

Chi-square test: Gender x Location (NSW Local Court) X² = 0.026 , df = 1 , p = 0.871

Plain text Summary. We examined wellbeing outcomes among NSW judicial officers by gender, court level, and location, looking at both all NSW judges and local court judges specifically. At the state level, women reported higher psychological distress and secondary traumatic stress than men before correction. However, after applying Bonferroni correction, only secondary traumatic stress remained significant. We found no significant differences in gender distribution across court levels. No significant location differences were found for either all NSW or local court judges, and the distribution of men and women across locations did not differ significantly at either level.

Wellbeing Measures by Gender (All NSW Judicial Officers) (N = 164): After Bonferroni correction, women reported significantly higher secondary traumatic stress than men (Women M = 35.92, Men M = 29.87; t(143.53) = -2.98, p = .003, p_adj = .010, d = 0.48). Women also reported higher psychological distress than men, but this did not survive correction (Women M = 18.43, Men M = 15.50; t(137.75) = -2.71, p = .008, p_adj = .023, d = 0.44). No significant difference was found for life satisfaction (t(152.42) = 0.57, p = .568, d = 0.09).

Wellbeing measure by Gender (All NSW Judicial Officers) (N = 164): After Bonferroni correction, women reported significantly higher secondary traumatic stress (Women M = 35.92, Men M = 29.87; t(143.53) = -2.98, p = .003, p_adj = .010, d = 0.48). No significant difference was found for life satisfaction (t(152.42) = 0.57, p = .568, d = 0.09).

Gender Distribution by Court Level (NSW): The proportion of men and women did not differ significantly across Supreme, District, and Local court levels (χ²(2) = 1.82, p = .403). Local court had the most even gender split (48.5% men, 51.5% women), while Supreme Court was more male dominated (61.5% men).

Wellbeing by Location (All NSW Judicial Officer): No significant differences were found between Metro and Regional/Remote NSW judicial officers on any wellbeing measure after correction. Psychological distress (t(48.12) = -0.17, p = .867, d = 0.03), secondary traumatic stress (t(49.40) = -0.11, p = .912, d = 0.02), and life satisfaction (t(44.38) = 0.21, p = .837, d = 0.04) were all comparable between groups with negligible effect sizes. The distribution of men and women across locations did not differ significantly (χ²(1) = 1.74, p = .187), with 13.9% of men and 23.6% of women located in Regional/Remote areas.

Wellbeing measure by Gender (NSW Local Court Only) (N = 69): No significant differences were found between men and women after Bonferroni correction. Women reported numerically higher psychological distress (Women M = 19.09, Men M = 16.34; t(62.4) = -1.69, p = .097, p_adj = .290, d = 0.41) and secondary traumatic stress (Women M = 36.09, Men M = 31.12; t(64.9) = -1.66, p = .101, p_adj = .304, d = 0.41) than men, with small to medium effect sizes, but neither survived correction. Life satisfaction was comparable between groups (t(65.8) = 0.08, p = .939, d = 0.02).

Wellbeing by Location (NSW Local Court Only): No significant differences were found between Metro and Regional/Remote local court officers on any wellbeing measure after correction. Psychological distress (t(52.29) = 1.00, p = .320, d = 0.25), secondary traumatic stress (t(48.46) = 0.02, p = .984, d = 0.01), and life satisfaction (t(45.51) = 0.00, p = .999, d = 0.00) were all comparable between groups with negligible to small effect sizes. The distribution of men and women across locations did not differ significantly (χ²(1) = 0.03, p = .871), with 32.1% of men and 37.5% of women in local court located in Regional/Remote areas.

Limitations. Running multiple tests increases the risk of Type I error. Bonferroni correction was applied to reduce this risk. The relatively small sample sizes for NSW local court (n = 69) and Regional/Remote NSW (n = 28 overall, n = 21 local court) substantially limit statistical power.

VIC Judicial Officers: Wellbeing measures by Gender and Location

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VIC total N: 173 VIC Local Court N: 91

VIC total N: 173

Gender Differences in Wellbeing: All VIC Judicial Officers (N = 173)
Scale	Mean Male	Mean Female	t	df	p	p (adjusted)	Cohen’s d
Psychological Distress (K10)	16.238	17.886	-1.718	163.075	0.088	0.263	0.266
Secondary Traumatic Stress (STSS)	30.488	34.092	-1.955	161.625	0.052	0.157	0.304
Satisfaction with Life (SWLS)	24.675	24.391	0.298	160.321	0.766	1.000	0.046
Note. • p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 3 tests (corrected threshold: p < .017).

Gender Distribution by Court Level: VIC Judicial Officers (N = 173)
Court Level	Male	Female
Supreme	24 (60%)	16 (40%)
District	15 (42.9%)	20 (57.1%)
Local	39 (44.8%)	48 (55.2%)

Chi-square test: Gender x Court Level (VIC) X² = 3.027 , df = 2 , p = 0.22

VIC Metro N (all courts): 151 VIC Regional N (all courts): 17

Wellbeing by Location: All VIC Judicial Officers
Scale	Metro (n=151)	Regional (n=17)	t	df	p	p (adjusted)	Cohen’s d
Psychological Distress (K10)	16.880	18.706	-1.074	19.311	0.296	0.888	0.290
Secondary Traumatic Stress (STSS)	32.420	32.647	-0.065	18.863	0.949	1.000	0.019
Satisfaction with Life (SWLS)	24.846	23.562	0.812	18.335	0.427	1.000	0.215
Note. • p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 3 tests (corrected threshold: p < .017).

Distribution of Metro vs Regional Location by Gender: All VIC Judicial Officers
Gender	Metro	Regional
Male	67 (85.9%)	11 (14.1%)
Female	81 (93.1%)	6 (6.9%)

Chi-square test: Gender x Location (All VIC) X² = 1.597 , df = 1 , p = 0.206

VIC Local Court N: 91

Gender Differences in Wellbeing: VIC Local Court (N = 91)
Scale	Mean Male	Mean Female	t	df	p	p (adjusted)	Cohen’s d
Psychological Distress (K10)	16.54	18.90	-1.80	82.9	0.075	0.225	0.387
Satisfaction with Life (SWLS)	24.03	23.02	0.70	74.5	0.487	1.000	0.154
Secondary Traumatic Stress (STSS)	30.03	36.38	-2.36	77.1	0.021	0.063	0.516
Note. • p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 3 tests (corrected threshold: p < .017).

VIC Local Court Metro N: 70 VIC Local Court Regional N: 16

Wellbeing by Location: VIC Local Court Judicial Officers
Scale	Metro (n=70)	Regional (n=16)	t	df	p	p (adjusted)	Cohen’s d
Psychological Distress (K10)	17.514	18.938	-0.764	21.027	0.453	1	0.225
Secondary Traumatic Stress (STSS)	33.929	32.812	0.290	20.866	0.775	1	0.086
Satisfaction with Life (SWLS)	23.868	23.667	0.113	20.978	0.911	1	0.032
Note. • p < .05, p < .01, * p < .001. p (adjusted) reflects Bonferroni correction across 3 tests (corrected threshold: p < .017).

Distribution of Metro vs Regional Location by Gender: VIC Local Court
Gender	Metro	Regional
Male	26 (72.2%)	10 (27.8%)
Female	41 (87.2%)	6 (12.8%)

Chi-square test: Gender x Location (VIC Local Court) X² = 2.066 , df = 1 , p = 0.151

Plain text Summary. We examined wellbeing outcomes among VIC judicial officers by gender, court level, and location, looking at both all VIC judges and local court judges specifically. No significant differences were found between men and women at either the state or local court level. No significant differences were found in gender distribution across court levels. No significant location differences were found for either all VIC or local court judges, and the distribution of men and women across locations did not differ significantly at either level.

Summary w/Statistics. We conducted independent samples t-tests to examine gender differences in wellbeing among all VIC judicial officers and among VIC local court officers specifically, and to compare wellbeing between Metro and Regional/Remote officers at both levels. Bonferroni correction was applied across 3 tests in each comparison (corrected threshold: p < .017).

Wellbeing Measures by Gender (All VIC Judicial Officers) (N = 173): No significant differences were found between men and women after Bonferroni correction on any wellbeing measure. Women reported numerically higher psychological distress (Women M = 17.89, Men M = 16.24; t(163.08) = -1.72, p = .088, p_adj = .263, d = 0.27) and secondary traumatic stress (Women M = 34.09, Men M = 30.49; t(161.63) = -1.96, p = .052, p_adj = .157, d = 0.30) than men, but neither survived correction. Life satisfaction was comparable between groups (t(160.32) = 0.30, p = .766, d = 0.05).

Gender Distribution by Court Level (VIC): The proportion of men and women did not differ significantly across Supreme, District, and Local court levels (χ²(2) = 3.03, p = .220). Supreme Court was more male dominated (60% men), while District and Local courts had more even or female majority splits (District: 42.9% men, 57.1% women; Local: 44.8% men, 55.2% women).

Wellbeing by Location (All VIC Judicial Officers): No significant differences were found between Metro and Regional/Remote VIC judicial officers on any wellbeing measure after correction. Psychological distress (t(19.31) = -1.07, p = .296, d = 0.29), secondary traumatic stress (t(18.86) = -0.07, p = .949, d = 0.02), and life satisfaction (t(18.34) = 0.81, p = .427, d = 0.22) were all comparable between groups. The distribution of men and women across locations did not differ significantly (χ²(1) = 1.60, p = .206), with 14.1% of men and 6.9% of women located in Regional/Remote areas.

Wellbeing Measures by Gender (VIC Local Court Only) (N = 91): No significant differences were found between men and women after Bonferroni correction. Women reported numerically higher psychological distress (Women M = 18.90, Men M = 16.54; t(82.9) = -1.80, p = .075, p_adj = .225, d = 0.39) than men. Men reported numerically higher secondary traumatic stress than women (Men M = 36.38, Women M = 30.03; t(77.1) = -2.36, p = .021, p_adj = .063, d = 0.52), with neither surviving correction. Life satisfaction was comparable between groups (t(74.5) = 0.70, p = .487, d = 0.15).

Wellbeing by Location (VIC Local Court Only): No significant differences were found between Metro and Regional/Remote local court officers on any wellbeing measure after correction. Psychological distress (t(21.03) = -0.76, p = .453, d = 0.23), secondary traumatic stress (t(20.87) = 0.29, p = .775, d = 0.09), and life satisfaction (t(20.98) = 0.11, p = .911, d = 0.03) were all comparable between groups with negligible to small effect sizes. The distribution of men and women across locations did not differ significantly (χ²(1) = 2.07, p = .151), with 27.8% of men and 12.8% of women in local court located in Regional/Remote areas.

Limitations. Running multiple tests increases the risk of Type I error. Bonferroni correction was applied to reduce this risk.

Kate: JAWS Item 44 & 47 by Gender, Court Level, Region, & State

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RAW, Items 44 & 47, Overall

                                    Item   N Mean   SD
1                44. Sentencing (Stress) 502 3.18 1.01
2          44. Sentencing (Satisfaction) 502 3.59 0.89
3       47. Graphic AV evidence (Stress) 517 2.86 1.12
4 47. Graphic AV evidence (Satisfaction) 503 1.64 0.85

RAW, Items 44 & 47 by Gender

  Gender                                   Item   N Mean   SD
1 Female                44. Sentencing (Stress) 248 3.38 0.97
2   Male                44. Sentencing (Stress) 245 2.98 1.02
3 Female          44. Sentencing (Satisfaction) 248 3.57 0.83
4   Male          44. Sentencing (Satisfaction) 245 3.62 0.93
5 Female       47. Graphic AV evidence (Stress) 259 3.09 1.13
6   Male       47. Graphic AV evidence (Stress) 250 2.61 1.07
7 Female 47. Graphic AV evidence (Satisfaction) 253 1.64 0.86
8   Male 47. Graphic AV evidence (Satisfaction) 242 1.65 0.84

RAW, Items 44 & 47 by Court Level

   Court Level                                   Item   N Mean   SD
1        Local                44. Sentencing (Stress) 255 3.14 0.98
2     District                44. Sentencing (Stress) 134 3.35 1.01
3      Supreme                44. Sentencing (Stress)  79 3.04 1.09
4        Local          44. Sentencing (Satisfaction) 255 3.70 0.86
5     District          44. Sentencing (Satisfaction) 134 3.59 0.79
6      Supreme          44. Sentencing (Satisfaction)  79 3.28 1.02
7        Local       47. Graphic AV evidence (Stress) 242 2.89 1.11
8     District       47. Graphic AV evidence (Stress) 136 3.10 1.11
9      Supreme       47. Graphic AV evidence (Stress)  90 2.43 0.99
10       Local 47. Graphic AV evidence (Satisfaction) 235 1.76 0.89
11    District 47. Graphic AV evidence (Satisfaction) 134 1.53 0.75
12     Supreme 47. Graphic AV evidence (Satisfaction)  87 1.52 0.79

RAW, Items 44 & 47 by Region

    Region                                   Item   N Mean   SD
1    Metro                44. Sentencing (Stress) 419 3.16 1.02
2 Regional                44. Sentencing (Stress)  62 3.37 0.89
3    Metro          44. Sentencing (Satisfaction) 419 3.58 0.89
4 Regional          44. Sentencing (Satisfaction)  62 3.71 0.82
5    Metro       47. Graphic AV evidence (Stress) 436 2.86 1.13
6 Regional       47. Graphic AV evidence (Stress)  58 2.81 1.07
7    Metro 47. Graphic AV evidence (Satisfaction) 425 1.63 0.85
8 Regional 47. Graphic AV evidence (Satisfaction)  55 1.69 0.90

RAW, Items 44 & 47 by State

          State                                   Item   N Mean   SD
1           NSW                44. Sentencing (Stress) 137 3.28 0.97
2           VIC                44. Sentencing (Stress) 136 3.21 1.08
3           QLD                44. Sentencing (Stress)  91 2.90 1.01
4            WA                44. Sentencing (Stress)  43 3.23 1.04
5            SA                44. Sentencing (Stress)  47 3.28 0.93
6           TAS                44. Sentencing (Stress)  18 3.06 1.00
7           ACT                44. Sentencing (Stress)  14 3.43 0.76
8            NT                44. Sentencing (Stress)  14 3.21 0.97
9  Pref Not Say                44. Sentencing (Stress)   2   NA   NA
10          NSW          44. Sentencing (Satisfaction) 137 3.59 0.92
11          VIC          44. Sentencing (Satisfaction) 136 3.66 0.90
12          QLD          44. Sentencing (Satisfaction)  91 3.54 0.75
13           WA          44. Sentencing (Satisfaction)  43 3.37 1.02
14           SA          44. Sentencing (Satisfaction)  47 3.57 0.88
15          TAS          44. Sentencing (Satisfaction)  18 3.83 0.92
16          ACT          44. Sentencing (Satisfaction)  14 3.57 0.85
17           NT          44. Sentencing (Satisfaction)  14 3.43 1.02
18 Pref Not Say          44. Sentencing (Satisfaction)   2   NA   NA
19          NSW       47. Graphic AV evidence (Stress) 141 2.92 1.16
20          VIC       47. Graphic AV evidence (Stress) 147 2.94 1.16
21          QLD       47. Graphic AV evidence (Stress)  91 2.65 1.12
22           WA       47. Graphic AV evidence (Stress)  49 2.98 1.11
23           SA       47. Graphic AV evidence (Stress)  43 2.81 0.98
24          TAS       47. Graphic AV evidence (Stress)  17 2.35 1.00
25          ACT       47. Graphic AV evidence (Stress)  13 3.15 0.80
26           NT       47. Graphic AV evidence (Stress)  14 2.93 0.73
27 Pref Not Say       47. Graphic AV evidence (Stress)   2   NA   NA
28          NSW 47. Graphic AV evidence (Satisfaction) 135 1.70 0.84
29          VIC 47. Graphic AV evidence (Satisfaction) 144 1.65 0.85
30          QLD 47. Graphic AV evidence (Satisfaction)  89 1.76 0.95
31           WA 47. Graphic AV evidence (Satisfaction)  48 1.29 0.54
32           SA 47. Graphic AV evidence (Satisfaction)  42 1.57 0.80
33          TAS 47. Graphic AV evidence (Satisfaction)  16 1.88 0.89
34          ACT 47. Graphic AV evidence (Satisfaction)  13 1.46 0.66
35           NT 47. Graphic AV evidence (Satisfaction)  14 1.57 1.09
36 Pref Not Say 47. Graphic AV evidence (Satisfaction)   2   NA   NA

Sharyn II: JAWS Correlates

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Exploratory Factor Analysis: JAWS Most stressful (in progress)

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Exploratory Factor Analysis: JAWS Most Satisfying (in progress)

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Exploratory Factor Analysis: JAWS difference by satisfaction (in progress)

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Exploratory Factor Analysis: JAWS Difference by Distress

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Step 1: Sample Size

N = 576

Field suggested that more than 200 participants are required for the scree plot to be reliable.

Step 2: Correlation Matrix

No correlations were higher than .90., so no concerns about multicollinearity in the data.

Step 3: Bartlett’s Test of Sphericity

Chi-square = 13847.49 , df = 1128 , p < .001

significant p value suggests it is not an identity matrix and appropriate for factor analysis.

Step 4: Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy

KMO Measure of Sampling Adequacy (Overall MSA = 0.93)
	Item	MSA
JAWS_1_NewDiff	JAWS_1_NewDiff	0.95
JAWS_2_NewDiff	JAWS_2_NewDiff	0.97
JAWS_3_NewDiff	JAWS_3_NewDiff	0.94
JAWS_4_NewDiff	JAWS_4_NewDiff	0.93
JAWS_5_NewDiff	JAWS_5_NewDiff	0.95
JAWS_6_NewDiff	JAWS_6_NewDiff	0.94
JAWS_7_NewDiff	JAWS_7_NewDiff	0.94
JAWS_8_NewDiff	JAWS_8_NewDiff	0.94
JAWS_9_NewDiff	JAWS_9_NewDiff	0.95
JAWS_10_NewDiff	JAWS_10_NewDiff	0.95
JAWS_11_NewDiff	JAWS_11_NewDiff	0.94
JAWS_12_NewDiff	JAWS_12_NewDiff	0.94
JAWS_13_NewDiff	JAWS_13_NewDiff	0.94
JAWS_14_NewDiff	JAWS_14_NewDiff	0.94
JAWS_15_NewDiff	JAWS_15_NewDiff	0.94
JAWS_16_NewDiff	JAWS_16_NewDiff	0.96
JAWS_17_NewDiff	JAWS_17_NewDiff	0.91
JAWS_18_NewDiff	JAWS_18_NewDiff	0.81
JAWS_19_NewDiff	JAWS_19_NewDiff	0.94
JAWS_20_NewDiff	JAWS_20_NewDiff	0.95
JAWS_21_NewDiff	JAWS_21_NewDiff	0.91
JAWS_22_NewDiff	JAWS_22_NewDiff	0.90
JAWS_23_NewDiff	JAWS_23_NewDiff	0.94
JAWS_24_NewDiff	JAWS_24_NewDiff	0.92
JAWS_25_NewDiff	JAWS_25_NewDiff	0.96
JAWS_26_NewDiff	JAWS_26_NewDiff	0.94
JAWS_27_NewDiff	JAWS_27_NewDiff	0.95
JAWS_28_NewDiff	JAWS_28_NewDiff	0.93
JAWS_29_NewDiff	JAWS_29_NewDiff	0.92
JAWS_30_NewDiff	JAWS_30_NewDiff	0.90
JAWS_31_NewDiff	JAWS_31_NewDiff	0.95
JAWS_32_NewDiff	JAWS_32_NewDiff	0.86
JAWS_33_NewDiff	JAWS_33_NewDiff	0.92
JAWS_34_NewDiff	JAWS_34_NewDiff	0.92
JAWS_35_NewDiff	JAWS_35_NewDiff	0.96
JAWS_36_NewDiff	JAWS_36_NewDiff	0.95
JAWS_37_NewDiff	JAWS_37_NewDiff	0.91
JAWS_38_NewDiff	JAWS_38_NewDiff	0.91
JAWS_39_NewDiff	JAWS_39_NewDiff	0.93
JAWS_40_NewDiff	JAWS_40_NewDiff	0.92
JAWS_41_NewDiff	JAWS_41_NewDiff	0.93
JAWS_42_NewDiff	JAWS_42_NewDiff	0.96
JAWS_43_NewDiff	JAWS_43_NewDiff	0.95
JAWS_44_NewDiff	JAWS_44_NewDiff	0.94
JAWS_45_NewDiff	JAWS_45_NewDiff	0.81
JAWS_46_NewDiff	JAWS_46_NewDiff	0.79
JAWS_47_NewDiff	JAWS_47_NewDiff	0.90
JAWS_48_NewDiff	JAWS_48_NewDiff	0.91

Individual item MSAs ranged from 0.79 to 0.97

Field suggested that a .5 is bare minimum and value above > .9 is superb.

Step 5: Determining the Number of Factors

Parallel analysis and scree plot were used to determine the number of factors.

Parallel analysis suggests that the number of factors = 10 and the number of components = NA Parallel analysis suggested 10 factors and typically considered more rigorous than scree plot, but running both 9 and 10 factors show that 9-factor has better BIC (model fit).Field mentioned that scree plot can underextract when the item set is larger and a 2 to 3 factor might be hard to interpre them meaningfully

Tried 8-factor fit. Better BIC but merged factors in incoherent ways e.g., safety and security (37, 38) with employment perks (31, 33), and physical workplace conditions (1, 2, & 6). Field did say that factors should make conceptual sense, not just fit best

Model Fit Comparison: 8 to 11 Factor Solutions
Number of Factors	RMSEA	TLI	BIC
8	0.053	0.856	-2893.285
9	0.051	0.866	-2827.793
10	0.049	0.877	-2762.498
11	0.047	0.886	-2681.155

Step 7: Factor Loadings

Loadings below .30 are suppressed. Items are sorted by factor.

Note: JAWS46 living away from home might be a heywood case - theoretically impossible & items 2, 3, 7, 18, 20, 28, 30, 35, 39, 42 have no loading above .3, so they are not explained well by any factors. Ask richard to report 46 as limitation or remove? Removing it made BIC worse and weakened the circuit travel factor + ML8

Factor Loadings: JAWS NewDiff EFA (9-Factor Solution, Loadings < .30 Suppressed)
	Item	Vicarious Trauma	Cognitive Demands	Court Staff Relationships	Collegial Inclusion	Workload & Pace	Court Leadership	Employment Conditions	Safety & Security	Circuit & Travel
JAWS_1_NewDiff	Adequacy of staffing	NA	NA	0.327	NA	NA	NA	NA	NA	NA
JAWS_2_NewDiff	Physical court environment	NA	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_3_NewDiff	IT resources	NA	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_4_NewDiff	Workload	NA	NA	NA	NA	0.758	NA	NA	NA	NA
JAWS_5_NewDiff	Control over working day	NA	NA	NA	NA	0.424	NA	0.311	NA	NA
JAWS_6_NewDiff	Adequacy of breaks	NA	NA	NA	NA	0.419	NA	NA	NA	NA
JAWS_7_NewDiff	Frequency of interruptions	NA	NA	NA	NA	0.312	NA	NA	NA	NA
JAWS_8_NewDiff	Pace of work	NA	NA	NA	NA	0.738	NA	NA	NA	NA
JAWS_9_NewDiff	Fairness of work distribution	NA	NA	NA	NA	0.302	0.310	NA	NA	NA
JAWS_10_NewDiff	Pace of colleagues	NA	NA	NA	0.391	NA	NA	NA	NA	NA
JAWS_11_NewDiff	Complex decisions	NA	0.741	NA	NA	NA	NA	NA	NA	NA
JAWS_12_NewDiff	Writing judgments	NA	0.689	NA	NA	NA	NA	NA	NA	NA
JAWS_13_NewDiff	Assessing credibility	NA	0.397	NA	NA	NA	NA	NA	NA	NA
JAWS_14_NewDiff	Assessing legal argument	NA	0.649	NA	NA	NA	NA	NA	NA	NA
JAWS_15_NewDiff	Emotional case content	0.652	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_16_NewDiff	Quality of legal representation	NA	NA	NA	NA	NA	NA	0.306	NA	NA
JAWS_17_NewDiff	Self-represented litigants	0.404	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_18_NewDiff	Managing juries	NA	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_19_NewDiff	Emotions of parties/witnesses	0.494	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_20_NewDiff	Isolation of judicial role	NA	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_21_NewDiff	Working with colleagues	NA	NA	NA	0.813	NA	NA	NA	NA	NA
JAWS_22_NewDiff	Working with court staff	NA	NA	0.811	NA	NA	NA	NA	NA	NA
JAWS_23_NewDiff	Leadership role	NA	NA	NA	0.327	NA	NA	NA	NA	NA
JAWS_24_NewDiff	Dealing with other staff	NA	NA	0.835	NA	NA	NA	NA	NA	NA
JAWS_25_NewDiff	Privilege and status	0.402	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_26_NewDiff	Impact of decisions	0.570	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_27_NewDiff	Judiciary & administration	NA	NA	0.644	NA	NA	NA	NA	NA	NA
JAWS_28_NewDiff	Appellate review	NA	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_29_NewDiff	Judicial leadership	NA	NA	NA	NA	NA	0.755	NA	NA	NA
JAWS_30_NewDiff	Professional activities outside court	NA	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_31_NewDiff	Leave entitlements	NA	NA	NA	NA	NA	NA	0.450	NA	NA
JAWS_32_NewDiff	Remuneration	NA	NA	NA	NA	NA	NA	0.413	NA	NA
JAWS_33_NewDiff	Financial benefits	NA	NA	NA	NA	NA	NA	0.625	NA	NA
JAWS_34_NewDiff	Media commentary	0.472	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_35_NewDiff	Working with values	NA	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_36_NewDiff	Positive contribution to society	0.331	0.304	NA	NA	NA	NA	NA	NA	NA
JAWS_37_NewDiff	Safety in court	NA	NA	NA	NA	NA	NA	NA	0.838	NA
JAWS_38_NewDiff	Safety beyond court	NA	NA	NA	NA	NA	NA	NA	0.673	NA
JAWS_39_NewDiff	AG public support	NA	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_40_NewDiff	Rehabilitation programs	0.358	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_41_NewDiff	Work valued by leadership	NA	NA	NA	NA	NA	0.690	NA	NA	NA
JAWS_42_NewDiff	Professional development	NA	NA	NA	NA	NA	0.300	NA	NA	NA
JAWS_43_NewDiff	Formal judicial education	NA	NA	NA	0.333	NA	NA	NA	NA	NA
JAWS_44_NewDiff	Sentencing	0.460	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_45_NewDiff	Going on circuit	NA	NA	NA	NA	NA	NA	NA	NA	0.657
JAWS_46_NewDiff	Living away from home	NA	NA	NA	NA	NA	NA	NA	NA	1.013
JAWS_47_NewDiff	Graphic audiovisual evidence	0.550	NA	NA	NA	NA	NA	NA	NA	NA
JAWS_48_NewDiff	Camaraderie	NA	NA	NA	0.716	NA	NA	NA	NA	NA

Items were included in the factor summary based on two requirements in Field’s textbook: a) primary loading > .5 & no secondary loading of > .30. I’ll still show the cross-loading in the heat map, but not here.

Table. Exploratory Factor Analysis: JAWS NewDiff Scores (9-Factor Solution)
Factor	Placeholder Names	Items	Variance Explained
ML9	Vicarious Trauma & Case Content	Emotional content of cases; Impact of decisions on people’s lives; Graphic audiovisual evidence	16.3%
ML3	Cognitive Demands	Making complex decisions; Formulating reasons/writing judgments; Assessing legal argument	12.3%
ML1	Court Staff Relationships	Working with other court staff; Dealing with other staff in the court; Relationship between judiciary and court administration	12.3%
ML4	Collegial Community	Working with judicial colleagues; Camaraderie and inclusion by colleagues	11.6%
ML6	Workload & Pace	Workload; Pace at which I am required to work	11.3%
ML7	Court Leadership & Recognition	Judicial leadership within my court; Degree to which my work is valued by court leadership	9.8%
ML2	Employment perks	Adequacy of remuneration/financial benefits; Leave entitlements	9.4%
ML8	Safety & Security	Safety in court; Safety beyond court precinct	9.1%
ML5	Travel	Going on circuit or travelling in regional and remote communities; Living away from home for court work	7.8%

Step 8: Model Fit

RMSEA = 0.051 TLI = 0.866 BIC = -2827.793

RMSEA of 0.051 is just above the .05 threshold but within acceptable range. TLI of 0.866 is below the ideal .95 but acceptable for a large item set.

Step 9: Rotation Check — Oblimin vs Varimax

To confirm oblimin was the appropriate rotation, a varimax solution was also run for comparison.

Loadings: ML3 ML9 ML2 ML7 ML5 ML4 ML1 ML6 ML8
JAWS_15_NewDiff 0.697
JAWS_19_NewDiff 0.584
JAWS_26_NewDiff 0.649
JAWS_34_NewDiff 0.514
JAWS_44_NewDiff 0.556 0.312
JAWS_47_NewDiff 0.542
JAWS_31_NewDiff 0.604
JAWS_33_NewDiff 0.687
JAWS_21_NewDiff 0.793
JAWS_48_NewDiff 0.737
JAWS_11_NewDiff 0.715
JAWS_12_NewDiff 0.653
JAWS_14_NewDiff 0.623
JAWS_22_NewDiff 0.318 0.756
JAWS_24_NewDiff 0.754
JAWS_27_NewDiff 0.605
JAWS_4_NewDiff 0.707
JAWS_8_NewDiff 0.345 0.677
JAWS_45_NewDiff 0.635
JAWS_46_NewDiff 0.953
JAWS_37_NewDiff 0.736
JAWS_38_NewDiff 0.606
JAWS_29_NewDiff 0.332 0.319 0.645 JAWS_41_NewDiff 0.352 0.363 0.602 JAWS_1_NewDiff 0.470 0.353
JAWS_2_NewDiff 0.394
JAWS_3_NewDiff
JAWS_5_NewDiff 0.459 0.427
JAWS_6_NewDiff 0.415 0.413
JAWS_7_NewDiff 0.353 0.303
JAWS_9_NewDiff 0.357 0.335 0.307 JAWS_10_NewDiff 0.406
JAWS_13_NewDiff 0.402 0.410
JAWS_16_NewDiff 0.317 0.380
JAWS_17_NewDiff 0.458
JAWS_18_NewDiff
JAWS_20_NewDiff 0.402
JAWS_23_NewDiff 0.449
JAWS_25_NewDiff 0.430
JAWS_28_NewDiff 0.337
JAWS_30_NewDiff
JAWS_32_NewDiff 0.464
JAWS_35_NewDiff 0.323 0.394
JAWS_36_NewDiff 0.417 0.352 0.347
JAWS_39_NewDiff 0.304
JAWS_40_NewDiff 0.378
JAWS_42_NewDiff 0.360 0.395
JAWS_43_NewDiff 0.412

             ML3   ML9   ML2   ML7   ML5   ML4   ML1   ML6   ML8

SS loadings 4.987 3.547 3.510 2.730 2.525 2.103 1.644 1.537 1.383 Proportion Var 0.104 0.074 0.073 0.057 0.053 0.044 0.034 0.032 0.029 Cumulative Var 0.104 0.178 0.251 0.308 0.360 0.404 0.438 0.470 0.499

There are more cross-loading, which can make interpreation harder. ML8 is weaker in this model, so the Oblimin solution might be a better rotation choice given its clearer, more interpretable factors.

Step 10: Factor Correlations

Factor Correlation Matrix (Phi)
	Vicarious Trauma	Cognitive Demands	Court Staff Relationships	Collegial community	Workload & Pace	Court Leadership	Employment perks	Safety & Security	Travel
Vicarious Trauma	1.00	0.46	0.36	0.23	0.39	0.19	0.19	0.30	0.29
Cognitive Demands	0.46	1.00	0.33	0.39	0.41	0.23	0.23	0.27	0.20
Court Staff Relationships	0.36	0.33	1.00	0.50	0.32	0.38	0.25	0.40	0.17
Collegial community	0.23	0.39	0.50	1.00	0.21	0.46	0.29	0.34	0.18
Workload & Pace	0.39	0.41	0.32	0.21	1.00	0.35	0.35	0.34	0.34
Court Leadership	0.19	0.23	0.38	0.46	0.35	1.00	0.41	0.37	0.22
Employment perks	0.19	0.23	0.25	0.29	0.35	0.41	1.00	0.45	0.22
Safety & Security	0.30	0.27	0.40	0.34	0.34	0.37	0.45	1.00	0.29
Travel	0.29	0.20	0.17	0.18	0.34	0.22	0.22	0.29	1.00

Field suggested oblimin rotation is the best choice for factor correlations >.30.

Step 11: Residual Analysis

Total residuals: 1128 Residuals > .05: 95 Percentage > .05: 8.4 %

Only 8.4 Reproduced correlation matrix shows how well the factor solution matches the actual correlations in the data. Field suggested that less than 50% of the residuals should be above .05 for it to be considered good fit. Only 8.4% of the residuals were over .05, lower than the 50% threshold. This shows that 9-factor soultion is reproducing in the original correlation matrix well. The smmetrical distribution centred at Zero suggests the difference between what the factor solution predicts and what’s actually in the data are small, no systematic over or under-predicting correlations.

Exploratory Factor Analysis: JAWS Difference by Distress (v2)

expand

Step 1: Sample Size

Respondents with ≥50% of JAWS NewDiff items completed: N = 576 ( 0 excluded for >50% missing).

Cases per item = 12

Field suggests 200+ for a reliable scree plot and notes that sampling adequacy is better judged by KMO (see Step 4) than by N alone.

Step 2: Correlation Matrix

No item pairs correlated above .90.

Determinant of the correlation matrix = 1.68e-11

Below Field’s threshold, but this is expected with 48 items; no correlations exceeded .90 and KMO is strong, so no items were removed.

43.4 % of item correlations exceeded .30 (mean |r| = 0.29 ), indicating the items share enough common variance to factor analyse.

Step 3: Bartlett’s Test of Sphericity

Chi-square = 13,847.49 , df = 1128 , p < .001

A significant result indicates the correlation matrix differs from an identity matrix (i.e., items do correlate), so factor analysis is appropriate. Note this test is almost always significant in large samples, so it is a minimum requirement rather than strong evidence of factorability — the KMO (Step 4) and the proportion of correlations above .30 (Step 2) carry more weight.

Step 4: Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy

KMO Measure of Sampling Adequacy (Overall MSA = 0.93), sorted lowest to highest
Item	MSA
JAWS_46_NewDiff	0.79
JAWS_18_NewDiff	0.81
JAWS_45_NewDiff	0.81
JAWS_32_NewDiff	0.86
JAWS_22_NewDiff	0.90
JAWS_30_NewDiff	0.90
JAWS_47_NewDiff	0.90
JAWS_17_NewDiff	0.91
JAWS_21_NewDiff	0.91
JAWS_37_NewDiff	0.91
JAWS_38_NewDiff	0.91
JAWS_48_NewDiff	0.91
JAWS_24_NewDiff	0.92
JAWS_29_NewDiff	0.92
JAWS_33_NewDiff	0.92
JAWS_34_NewDiff	0.92
JAWS_40_NewDiff	0.92
JAWS_4_NewDiff	0.93
JAWS_28_NewDiff	0.93
JAWS_39_NewDiff	0.93
JAWS_41_NewDiff	0.93
JAWS_3_NewDiff	0.94
JAWS_6_NewDiff	0.94
JAWS_7_NewDiff	0.94
JAWS_8_NewDiff	0.94
JAWS_11_NewDiff	0.94
JAWS_12_NewDiff	0.94
JAWS_13_NewDiff	0.94
JAWS_14_NewDiff	0.94
JAWS_15_NewDiff	0.94
JAWS_19_NewDiff	0.94
JAWS_23_NewDiff	0.94
JAWS_26_NewDiff	0.94
JAWS_44_NewDiff	0.94
JAWS_1_NewDiff	0.95
JAWS_5_NewDiff	0.95
JAWS_9_NewDiff	0.95
JAWS_10_NewDiff	0.95
JAWS_20_NewDiff	0.95
JAWS_27_NewDiff	0.95
JAWS_31_NewDiff	0.95
JAWS_36_NewDiff	0.95
JAWS_43_NewDiff	0.95
JAWS_16_NewDiff	0.96
JAWS_25_NewDiff	0.96
JAWS_35_NewDiff	0.96
JAWS_42_NewDiff	0.96
JAWS_2_NewDiff	0.97

Overall MSA = 0.93 ; individual item MSAs ranged from 0.79 to 0.97

No items fell below Field’s .50 minimum, so all 48 items were retained for extraction. Field’s benchmarks: .50 bare minimum; .70–.80 good; .80–.90 great; above .90 superb.

Step 5: Determining the Number of Factors

Parallel analysis and the scree plot were used to determine the number of factors, with candidate solutions compared on model fit and interpretability.

Parallel analysis suggests that the number of factors = 10 and the number of components = NA

Model Fit Comparison: 8 to 11 Factor Solutions (lower BIC = better)
Number of Factors	RMSEA	TLI	BIC
8	0.053	0.856	-2893.3
9	0.051	0.866	-2827.8
10	0.049	0.877	-2762.5
11	0.047	0.886	-2681.2

The scree plot showed one dominant factor followed by an inflexion at around 3–4 factors; however, scree and eigenvalue rules are known to underextract with large item sets, and solutions this small produced broad, uninterpretable factors. Parallel analysis, generally the more rigorous criterion, suggested 10 factors, though the actual and resampled eigenvalues were nearly indistinguishable from around the 7th factor onward, indicating diminishing returns across the 8–11 region. Candidate solutions from 8 to 11 factors were therefore compared on fit and interpretability. BIC favoured fewer factors (8 lowest), and 9 fit better than 10. However, the 8-factor solution merged conceptually distinct content e.g., safety and security (items 37, 38) with employment perks (31, 33) and physical workplace conditions (1, 2, 6). Following Field’s principle that factors must make conceptual sense rather than only optimise fit, the 9-factor solution was retained as the better balance (maybe? not sure about still. Check after running the other FAs) of parsimony, fit, and interpretability.

Step 6: Solution Diagnostics

Highest communality = 0.995 ( JAWS_46_NewDiff )

All communalities were below 1, indicating a proper (admissible) solution. Note: in oblique rotations, pattern loadings are regression weights and may legitimately exceed 1 when factors are correlated; this does not indicate a Heywood case provided communalities remain below 1.

Step 7: Factor Loadings

Items were assigned to factors using two criteria: (a) primary loading ≥ .40 (Stevens’ criterion, cited in Field) and (b) no secondary loading ≥ .30. Cross-loading items are shown in the heatmap but excluded from factor membership.

Factor Loadings: JAWS NewDiff EFA (9-Factor Solution, Loadings < .30 Suppressed)
Item	Emotional Weight & Public Exposure of the Role	Cognitive Demands	Court Staff Relationships	Collegial Community	Workload & Pace	Court Leadership & Recognition	Employment Perks	Safety & Security	Circuit & Travel
Adequacy of staffing	NA	NA	0.327	NA	NA	NA	NA	NA	NA
Physical court environment	NA	NA	NA	NA	NA	NA	NA	NA	NA
IT resources	NA	NA	NA	NA	NA	NA	NA	NA	NA
Workload	NA	NA	NA	NA	0.758	NA	NA	NA	NA
Control over working day	NA	NA	NA	NA	0.424	NA	0.311	NA	NA
Adequacy of breaks	NA	NA	NA	NA	0.419	NA	NA	NA	NA
Frequency of interruptions	NA	NA	NA	NA	0.312	NA	NA	NA	NA
Pace of work	NA	NA	NA	NA	0.738	NA	NA	NA	NA
Fairness of work distribution	NA	NA	NA	NA	0.302	0.310	NA	NA	NA
Pace of colleagues	NA	NA	NA	0.391	NA	NA	NA	NA	NA
Complex decisions	NA	0.741	NA	NA	NA	NA	NA	NA	NA
Writing judgments	NA	0.689	NA	NA	NA	NA	NA	NA	NA
Assessing credibility	NA	0.397	NA	NA	NA	NA	NA	NA	NA
Assessing legal argument	NA	0.649	NA	NA	NA	NA	NA	NA	NA
Emotional case content	0.652	NA	NA	NA	NA	NA	NA	NA	NA
Quality of legal representation	NA	NA	NA	NA	NA	NA	0.306	NA	NA
Self-represented litigants	0.404	NA	NA	NA	NA	NA	NA	NA	NA
Managing juries	NA	NA	NA	NA	NA	NA	NA	NA	NA
Emotions of parties/witnesses	0.494	NA	NA	NA	NA	NA	NA	NA	NA
Isolation of judicial role	NA	NA	NA	NA	NA	NA	NA	NA	NA
Working with colleagues	NA	NA	NA	0.813	NA	NA	NA	NA	NA
Working with court staff	NA	NA	0.811	NA	NA	NA	NA	NA	NA
Leadership role	NA	NA	NA	0.327	NA	NA	NA	NA	NA
Dealing with other staff	NA	NA	0.835	NA	NA	NA	NA	NA	NA
Privilege and status	0.402	NA	NA	NA	NA	NA	NA	NA	NA
Impact of decisions	0.570	NA	NA	NA	NA	NA	NA	NA	NA
Judiciary & administration	NA	NA	0.644	NA	NA	NA	NA	NA	NA
Appellate review	NA	NA	NA	NA	NA	NA	NA	NA	NA
Judicial leadership	NA	NA	NA	NA	NA	0.755	NA	NA	NA
Professional activities outside court	NA	NA	NA	NA	NA	NA	NA	NA	NA
Leave entitlements	NA	NA	NA	NA	NA	NA	0.450	NA	NA
Remuneration	NA	NA	NA	NA	NA	NA	0.413	NA	NA
Financial benefits	NA	NA	NA	NA	NA	NA	0.625	NA	NA
Media commentary	0.472	NA	NA	NA	NA	NA	NA	NA	NA
Working with values	NA	NA	NA	NA	NA	NA	NA	NA	NA
Positive contribution to society	0.331	0.304	NA	NA	NA	NA	NA	NA	NA
Safety in court	NA	NA	NA	NA	NA	NA	NA	0.838	NA
Safety beyond court	NA	NA	NA	NA	NA	NA	NA	0.673	NA
AG public support	NA	NA	NA	NA	NA	NA	NA	NA	NA
Rehabilitation programs	0.358	NA	NA	NA	NA	NA	NA	NA	NA
Work valued by leadership	NA	NA	NA	NA	NA	0.690	NA	NA	NA
Professional development	NA	NA	NA	NA	NA	0.300	NA	NA	NA
Formal judicial education	NA	NA	NA	0.333	NA	NA	NA	NA	NA
Sentencing	0.460	NA	NA	NA	NA	NA	NA	NA	NA
Going on circuit	NA	NA	NA	NA	NA	NA	NA	NA	0.657
Living away from home	NA	NA	NA	NA	NA	NA	NA	NA	1.013
Graphic audiovisual evidence	0.550	NA	NA	NA	NA	NA	NA	NA	NA
Camaraderie	NA	NA	NA	0.716	NA	NA	NA	NA	NA

Item Classification (membership: primary ≥ .40, secondary < .30)
Item	Factor	Primary	Secondary	Status
Living away from home	Circuit & Travel	1.01	0.03	Assigned
Going on circuit	Circuit & Travel	0.66	0.08	Assigned
Complex decisions	Cognitive Demands	0.74	0.11	Assigned
Writing judgments	Cognitive Demands	0.69	0.16	Assigned
Assessing legal argument	Cognitive Demands	0.65	0.12	Assigned
Assessing credibility	Cognitive Demands	0.40	0.25	Assigned
Appellate review	Cognitive Demands	0.27	0.24	Orphan — no salient loading
Working with colleagues	Collegial Community	0.81	0.07	Assigned
Camaraderie	Collegial Community	0.72	0.12	Assigned
Pace of colleagues	Collegial Community	0.39	0.17	Below .40 membership threshold
Leadership role	Collegial Community	0.33	0.21	Below .40 membership threshold
Formal judicial education	Collegial Community	0.33	0.18	Below .40 membership threshold
Professional activities outside court	Collegial Community	0.28	0.21	Orphan — no salient loading
Working with values	Collegial Community	0.28	0.21	Orphan — no salient loading
Judicial leadership	Court Leadership & Recognition	0.76	0.07	Assigned
Work valued by leadership	Court Leadership & Recognition	0.69	0.09	Assigned
Fairness of work distribution	Court Leadership & Recognition	0.31	0.30	Cross-loader — excluded
Professional development	Court Leadership & Recognition	0.30	0.22	Below .40 membership threshold
Managing juries	Court Leadership & Recognition	0.19	0.18	Orphan — no salient loading
Dealing with other staff	Court Staff Relationships	0.83	0.05	Assigned
Working with court staff	Court Staff Relationships	0.81	0.13	Assigned
Judiciary & administration	Court Staff Relationships	0.64	0.17	Assigned
Adequacy of staffing	Court Staff Relationships	0.33	0.29	Below .40 membership threshold
IT resources	Court Staff Relationships	0.23	0.18	Orphan — no salient loading
Emotional case content	Emotional Weight & Public Exposure of the Role	0.65	0.16	Assigned
Impact of decisions	Emotional Weight & Public Exposure of the Role	0.57	0.22	Assigned
Graphic audiovisual evidence	Emotional Weight & Public Exposure of the Role	0.55	0.13	Assigned
Emotions of parties/witnesses	Emotional Weight & Public Exposure of the Role	0.49	0.15	Assigned
Media commentary	Emotional Weight & Public Exposure of the Role	0.47	0.12	Assigned
Sentencing	Emotional Weight & Public Exposure of the Role	0.46	0.26	Assigned
Self-represented litigants	Emotional Weight & Public Exposure of the Role	0.40	0.10	Assigned
Privilege and status	Emotional Weight & Public Exposure of the Role	0.40	0.18	Assigned
Rehabilitation programs	Emotional Weight & Public Exposure of the Role	0.36	0.15	Below .40 membership threshold
Positive contribution to society	Emotional Weight & Public Exposure of the Role	0.33	0.30	Cross-loader — excluded
Isolation of judicial role	Emotional Weight & Public Exposure of the Role	0.27	0.15	Orphan — no salient loading
AG public support	Emotional Weight & Public Exposure of the Role	0.23	0.22	Orphan — no salient loading
Financial benefits	Employment Perks	0.62	0.12	Assigned
Leave entitlements	Employment Perks	0.45	0.19	Assigned
Remuneration	Employment Perks	0.41	0.14	Assigned
Quality of legal representation	Employment Perks	0.31	0.24	Below .40 membership threshold
Physical court environment	Employment Perks	0.26	0.19	Orphan — no salient loading
Safety in court	Safety & Security	0.84	0.05	Assigned
Safety beyond court	Safety & Security	0.67	0.08	Assigned
Workload	Workload & Pace	0.76	0.13	Assigned
Pace of work	Workload & Pace	0.74	0.10	Assigned
Control over working day	Workload & Pace	0.42	0.31	Cross-loader — excluded
Adequacy of breaks	Workload & Pace	0.42	0.26	Assigned
Frequency of interruptions	Workload & Pace	0.31	0.24	Below .40 membership threshold

Items not assigned to any factor:

No salient loading (< .30): 2. Physical court environment; 3. IT resources; 18. Managing juries; 20. Isolation of judicial role; 28. Appellate review; 30. Professional activities outside court; 35. Working with values; 39. AG public support
Cross-loaders (secondary ≥ .30): 5. Control over working day; 9. Fairness of work distribution; 36. Positive contribution to society
Primary in .30–.39 range: 1. Adequacy of staffing; 7. Frequency of interruptions; 10. Pace of colleagues; 16. Quality of legal representation; 23. Leadership role; 40. Rehabilitation programs; 42. Professional development; 43. Formal judicial education

Table. EFA Factor Summary: JAWS NewDiff (9-Factor Solution). Membership: primary ≥ .40, no secondary ≥ .30.
Factor	N Items	Items	% of Explained Variance
Court Leadership & Recognition	2	Judicial leadership; 41. Work valued by leadership	9.8%
Employment Perks	3	Leave entitlements; 32. Remuneration; 33. Financial benefits	9.4%
Safety & Security	2	Safety in court; 38. Safety beyond court	9.1%
Circuit & Travel	2	Going on circuit; 46. Living away from home	7.8%
Emotional Weight & Public Exposure of the Role	8	Emotional case content; 17. Self-represented litigants; 19. Emotions of parties/witnesses; 25. Privilege and status; 26. Impact of decisions; 34. Media commentary; 44. Sentencing; 47. Graphic audiovisual evidence	16.3%
Cognitive Demands	4	Complex decisions; 12. Writing judgments; 13. Assessing credibility; 14. Assessing legal argument	12.3%
Court Staff Relationships	3	Working with court staff; 24. Dealing with other staff; 27. Judiciary & administration	12.3%
Collegial Community	2	Working with colleagues; 48. Camaraderie	11.6%
Workload & Pace	3	Workload; 6. Adequacy of breaks; 8. Pace of work	11.3%

#### Step 8: Model Fit

RMSEA = 0.051 (90% CI [ 0.048 , 0.054 ]) TLI = 0.866 BIC = -2827.8 (used for model comparison in Step 5; no absolute benchmark)

RMSEA was within the .05–.08 range conventionally considered acceptable . TLI was below the conventional .90 benchmark,common for long multi-factor instruments. .

Step 9: Rotation Check — Oblimin vs Varimax

Oblique (oblimin) rotation was chosen a priori because facets of judicial work stress were expected to correlate — an expectation confirmed by the factor correlations in Step 10. As a sensitivity check, an orthogonal (varimax) solution was also examined.

Varimax (Orthogonal) Loadings for Comparison (< .30 Suppressed)
Item	ML3	ML9	ML2	ML7	ML5	ML4	ML1	ML6	ML8
Adequacy of staffing	NA	0.470	NA	NA	0.353	NA	NA	NA	NA
Physical court environment	NA	0.394	NA	NA	NA	NA	NA	NA	NA
IT resources	NA	NA	NA	NA	NA	NA	NA	NA	NA
Workload	NA	NA	NA	NA	NA	0.707	NA	NA	NA
Control over working day	NA	0.459	NA	NA	NA	0.427	NA	NA	NA
Adequacy of breaks	NA	0.415	NA	NA	NA	0.413	NA	NA	NA
Frequency of interruptions	0.353	NA	NA	NA	NA	0.303	NA	NA	NA
Pace of work	0.345	NA	NA	NA	NA	0.677	NA	NA	NA
Fairness of work distribution	NA	NA	0.357	NA	NA	0.335	NA	NA	0.307
Pace of colleagues	NA	NA	0.406	NA	NA	NA	NA	NA	NA
Complex decisions	NA	NA	NA	0.715	NA	NA	NA	NA	NA
Writing judgments	NA	NA	NA	0.653	NA	NA	NA	NA	NA
Assessing credibility	0.402	NA	NA	0.410	NA	NA	NA	NA	NA
Assessing legal argument	NA	NA	NA	0.623	NA	NA	NA	NA	NA
Emotional case content	0.697	NA	NA	NA	NA	NA	NA	NA	NA
Quality of legal representation	0.317	0.380	NA	NA	NA	NA	NA	NA	NA
Self-represented litigants	0.458	NA	NA	NA	NA	NA	NA	NA	NA
Managing juries	NA	NA	NA	NA	NA	NA	NA	NA	NA
Emotions of parties/witnesses	0.584	NA	NA	NA	NA	NA	NA	NA	NA
Isolation of judicial role	0.402	NA	NA	NA	NA	NA	NA	NA	NA
Working with colleagues	NA	NA	0.793	NA	NA	NA	NA	NA	NA
Working with court staff	NA	NA	0.318	NA	0.756	NA	NA	NA	NA
Leadership role	NA	NA	0.449	NA	NA	NA	NA	NA	NA
Dealing with other staff	NA	NA	NA	NA	0.754	NA	NA	NA	NA
Privilege and status	0.430	NA	NA	NA	NA	NA	NA	NA	NA
Impact of decisions	0.649	NA	NA	NA	NA	NA	NA	NA	NA
Judiciary & administration	NA	NA	NA	NA	0.605	NA	NA	NA	NA
Appellate review	0.337	NA	NA	NA	NA	NA	NA	NA	NA
Judicial leadership	NA	0.332	0.319	NA	NA	NA	NA	NA	0.645
Professional activities outside court	NA	NA	NA	NA	NA	NA	NA	NA	NA
Leave entitlements	NA	0.604	NA	NA	NA	NA	NA	NA	NA
Remuneration	NA	0.464	NA	NA	NA	NA	NA	NA	NA
Financial benefits	NA	0.687	NA	NA	NA	NA	NA	NA	NA
Media commentary	0.514	NA	NA	NA	NA	NA	NA	NA	NA
Working with values	0.323	NA	0.394	NA	NA	NA	NA	NA	NA
Positive contribution to society	0.417	NA	0.352	0.347	NA	NA	NA	NA	NA
Safety in court	NA	NA	NA	NA	NA	NA	NA	0.736	NA
Safety beyond court	NA	NA	NA	NA	NA	NA	NA	0.606	NA
AG public support	0.304	NA	NA	NA	NA	NA	NA	NA	NA
Rehabilitation programs	0.378	NA	NA	NA	NA	NA	NA	NA	NA
Work valued by leadership	NA	0.352	0.363	NA	NA	NA	NA	NA	0.602
Professional development	NA	0.360	0.395	NA	NA	NA	NA	NA	NA
Formal judicial education	NA	NA	0.412	NA	NA	NA	NA	NA	NA
Sentencing	0.556	NA	NA	0.312	NA	NA	NA	NA	NA
Going on circuit	NA	NA	NA	NA	NA	NA	0.635	NA	NA
Living away from home	NA	NA	NA	NA	NA	NA	0.953	NA	NA
Graphic audiovisual evidence	0.542	NA	NA	NA	NA	NA	NA	NA	NA
Camaraderie	NA	NA	0.737	NA	NA	NA	NA	NA	NA

Cross-loading items (secondary ≥ .30): oblimin = 3, varimax = 15. The same nine factors were identifiable in both solutions, but the varimax solution produced more cross-loadings — the expected consequence of forcing correlated factors to be orthogonal. The a priori choice of oblimin was retained.

Judges’ project

UNSW collaborators

last update: 08-06-2026

Quick Reference

Quick Reference

A Note on Interpreting Key Statistical Values (if you want the longer version)

Descriptive Statistics: Wellbeing Measures by Gender, Court Level, and Region

Judicial Attitudes to Work Scale (JAWS) Stress Scores by Gender, Court Level, and Region

Gender Differences across Wellbeing Measures

Court Level Differences across Wellbeing Measures

Regional Differences across Wellbeing Measures

Multiple Regression Predicting Wellbeing Outcomes using Court Level, Gender, and region

Correlational Analyses for Wellbeing measures

Judicial Officer Coping Strategy Use Across Gender, Court Level, and Region

Association between Judicial Officer Coping Strategy Use and wellbeing measures

Household and Labour Dynamics across Gender, Court Level, and Region

Psychosocial Safety Climate across Gender, Court Level, and Region

Sharyn: JAWS Response Patterns by Gender

Natalie: JAWS Court Leadership as Source of Satisfaction and Stress

Terese: Free Text Response Count by Gender

Kylie: JAWS additional items and Queensland Local Court Wellbeing Outcomes by Gender

Kylie II: Top Stressors and Satisfiers among Qld Judical officer

Wellbeing Measures in the Lower Court by Gender

NSW Judicial Officers: Wellbeing measures by Gender and Location

VIC Judicial Officers: Wellbeing measures by Gender and Location

Kate: JAWS Item 44 & 47 by Gender, Court Level, Region, & State

RAW, Items 44 & 47, Overall

RAW, Items 44 & 47 by Gender

RAW, Items 44 & 47 by Court Level

RAW, Items 44 & 47 by Region

RAW, Items 44 & 47 by State

Sharyn II: JAWS Correlates

Exploratory Factor Analysis: JAWS Most stressful (in progress)

Exploratory Factor Analysis: JAWS Most Satisfying (in progress)

Exploratory Factor Analysis: JAWS difference by satisfaction (in progress)

Exploratory Factor Analysis: JAWS Difference by Distress

Step 1: Sample Size

Step 2: Correlation Matrix

Step 3: Bartlett’s Test of Sphericity

Step 4: Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy

Step 5: Determining the Number of Factors

Step 7: Factor Loadings

Step 8: Model Fit

Step 9: Rotation Check — Oblimin vs Varimax

Step 10: Factor Correlations

Step 11: Residual Analysis

Exploratory Factor Analysis: JAWS Difference by Distress (v2)

Step 1: Sample Size

Step 2: Correlation Matrix

Step 3: Bartlett’s Test of Sphericity

Step 4: Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy

Step 5: Determining the Number of Factors

Step 6: Solution Diagnostics

Step 7: Factor Loadings

Step 9: Rotation Check — Oblimin vs Varimax