Days of Discovery 2026 - Expectations of Rejection and Emotion Dysregulation: Implications for Mental Health

Poster

knitr::include_graphics("ryan poster.jpg")

Poster Text

INTRODUCTION

Experiencing discrimination is a strong social predictor of paranoia, mistrust, and anxiety (Freeman & Loe, 2023). These experiences can cause an expectation of rejection, a phenomenon frequently observed among marginalized individuals such as transgender and gender diverse people, autistic people, and those belonging to racial and ethnic minority groups (Pellicane & Ciesla, 2022; Botha & Frost, 2020; Roach et al., 2023).

These experiences of mistrust and expectation of rejection may contribute to emotion dysregulation, often defined as a difficulty in effectively managing and responding to emotional experiences. Emotion dysregulation is associated with a range of adverse mental health outcomes (Crowell et al, 2015) and is an important target for clinical intervention. Previous research supports the idea that expectation of rejection is associated with emotion dysregulation: for instance, a systematic review focused on youth of color found a consistent association between race-related stress and emotion dysregulation (Roach et al., 2023). Similarly, a study of psychiatric patients and community participants found that rejection sensitivity and emotion dysregulation were positively associated (Velotti et al., 2015).

To better understand this relationship, we will study expectation of rejection and emotion dysregulation in a sample of college students and adults.

METHOD

For this study, we used Pearson’s r to examine the relationship between emotion dysregulation and the expectation of rejection in a sample of N = 184. Data was collected from a sample of college students and adults with no college experience in Spring of 2025. As part of a larger study on mental health and misinformation acceptance, we asked participants about their struggles with emotion regulation, the experiences with discrimination, and their expectation of rejection. Emotion dysregulation was divided into five subconstructs: difficulties with goal setting, emotional clarity, impulse control, coping strategies, and acceptance of emotions. All subconstructs were measured using the Difficulties in Emotion Regulation Scale (DERS-16; Bjureberg et al., 2016) on a 5-point Likert scale ranging from 1 (almost never) to 5 (almost always; for sample items, see Table 2). Expectation of rejection was assessed using a 4-item self-report measure (Link, 1987) on a 4-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree; for sample items, see Table 1).

RESULTS

Pearson correlations were used to examine the relationship between expectation of rejection and emotion dysregulation subconstructs. Results indicated that expectation of rejection was significantly associated with difficulties with goal setting r(182) = .41, p < .001; struggles with emotional clarity, r(182) = .47, p < .001; poor impulse control, r(182) = .44, p < .001; limited coping strategies, r(182) = .44, p < .001; and non-acceptance of emotions, r(182) = .40, p < .001. All associations were moderate in size and in the expected direction.

DISCUSSION

Our results indicate that greater expectation of rejection is associated with higher levels of emotion dysregulation across multiple subconstructs. This pattern is consistent with prior research suggesting that experiences of discrimination and mistrust may interfere with coping abilities. Anticipating rejection may increase emotional distress and limit coping strategies, contributing to difficulties with emotional functioning.

This has important implications for counseling and social work practice. Mental health professionals working with individuals who experience heightened expectations of rejection may benefit from directly addressing these beliefs. Interventions that target both cognitive appraisals and emotion regulation skills may be especially effective in improving outcomes. Supporting clients by reframing rejection expectations and strengthening emotion regulation abilities may be valuable focus points in the clinical environment, particularly for individuals from marginalized or socially stigmatized groups, where discrimination is a key social predictor of paranoia, mistrust, and anxiety. Overall, these findings highlight the importance of addressing rejection-related beliefs in both research and clinical practice.

REFERENCES

Bjureberg, J., Ljótsson, B., Tull, M. T., Hedman, E., Sahlin, H., Lundh, L.-G., Bjärehed, J., DiLillo, D., Messman-Moore, T., Gumpert, C. H., & Gratz, K. L. (2016). Development and Validation of a Brief Version of the Difficulties in Emotion Regulation Scale: The DERS-16. Journal of Psychopathology and Behavioral Assessment, 38(2), 284–296. https://doi.org/10.1007/s10862-015-9514-x

Botha, M., & Frost, D. M. (2020). Extending the Minority Stress Model to Understand Mental Health Problems Experienced by the Autistic Population. Society and Mental Health, 10(1), 20–34. https://doi.org/10.1177/2156869318804297

Crowell, S. E., Puzia, M. E., & Yaptangco, M. (2015). The ontogeny of chronic distress: Emotion dysregulation across the life span and its implications for psychological and physical health. Current Opinion in Psychology, 3, 91–99. https://doi.org/10.1016/j.copsyc.2015.03.023

Freeman, D., & Loe, B. S. (2023). Explaining paranoia: Cognitive and social processes in the occurrence of extreme mistrust. BMJ Mental Health, 26(1), e300880. https://doi.org/10.1136/bmjment-2023-300880

Link, B. G. (1987). Understanding labeling effects in the area of mental disorders: An assessment of the effects of expectations of rejection. American sociological review, 96-112.

Pellicane, M. J., & Ciesla, J. A. (2022). Associations between minority stress, depression, and suicidal ideation and attempts in transgender and gender diverse (TGD) individuals: Systematic review and meta-analysis. Clinical Psychology Review, 91, 102113. https://doi.org/10.1016/j.cpr.2021.102113

Roach, E. L., Haft, S. L., Huang, J., & Zhou, Q. (2023). Systematic Review: The Association Between Race-Related Stress and Trauma and Emotion Dysregulation in Youth of Color. Journal of the American Academy of Child & Adolescent Psychiatry, 62(2), 190–207. https://doi.org/10.1016/j.jaac.2022.04.013

Velotti, P., Garofalo, C., & Bizzi, F. (2015). Emotion Dysregulation Mediates the Relation between Mindfulness and Rejection Sensitivity. Psychiatria Danubina, 27(3), 259–272.

Code

Load Libraries

library(sjPlot)
library(ggplot2)
library(haven)
library(psych)
library(corrplot)

Load Data

df <- read.csv(file="data.csv", header=T)

Hypothesis 1

corr.out <- corr.test(subset(df, select=c(ers, ders_nonacceptance, ders_clarity, ders_goals, ders_strategies, ders_impulse)))

p <- ggplot(df, aes(x = ders_nonacceptance, y = ers)) +

  # Points
  geom_jitter(
    color = "#000000",
    alpha = 0.65,
    size = 4,
    shape = 16
  ) +

  # Labels
  labs(
    title = "Non-Acceptance & Expectation of Rejection",
    subtitle = "r(182) = .40, p < .001",
    x = "Non-Acceptance",
    y = "Expectation of Rejection"
  ) +

  # Clean theme
  theme_minimal(base_size = 13) +
  theme(
    plot.title    = element_text(face = "bold", margin = margin(b = 4)),
    plot.subtitle = element_text(color = "grey50", margin = margin(b = 12)),
    plot.caption  = element_text(color = "grey60"),
    axis.title    = element_text(color = "grey30"),
    panel.grid.minor  = element_blank(),
    panel.grid.major  = element_line(color = "grey92"),
    panel.background = element_rect(fill = "white"),
    plot.background  = element_rect(fill = "transparent", color = NA),
    plot.margin       = margin(20, 20, 20, 20)
  ) +
  theme(
    text = element_text(size = 12))

p

# 
# ggsave("ryan plot.svg", plot = p, width = 12.92, height = 7.49, bg = "transparent")

cor_data <- corr.out$r

labels <- c(
  "1 Expectation of Rejection",
  "2 Nonacceptance of Emotional Responses",
  "3 Lack of Emotional Clarity",
  "4 Difficulties Engaging in Goal-Directed Behavior",
  "5 Limited Access to Emotion Regulation Strategies",
  "6 Impulse Control Difficulties"
)

rownames(cor_data) <- labels
colnames(cor_data) <- 1:6

c <- corrplot(
  cor_data,
  method       = "color",        # filled squares
  type         = "upper",        # upper triangle only
  col          = COL2("RdBu"),   # red-to-blue diverging palette
  addCoef.col  = "white",        # white correlation numbers
  number.cex   = 0.85,           # size of numbers
  tl.col       = "black",        # axis label color
  cl.align.text = "l",
  cl.ratio     = 0.4,
  diag         = TRUE           # show diagonal
)