Clinical Readiness Analysis
Sean Collins
First created on 2022-01-31. Updated on 2022-02-03
Analysis performed for PSU Clinical Education Team (Kelly Legacy, Janine DeBaets, Tracey Houle)
# Load preliminary data
df <- read.csv("CRA1_Tool_Class24.csv")
df <- select(df, -(Summative))
df$ID <- as.integer(df$ID)
df$Class.Graduating.Year. <- as.factor(df$Class.Graduating.Year.)
df[sapply(df, is.character)] <- lapply(df[sapply(df, is.character)],
as.factor)
df$Semester <- 2
df$Semester <- as.factor(df$Semester)
df$Sex <- as.factor(df$Sex)# A function to order the factors
CRA_Score_Order <- function(variable) {
ordered(variable, levels = c("Not Yet at Beginner Level", "Beginner Level", "Developing Level", "Entry Level"))
}# Reodering the factors
df[4:13] <- lapply(df[4:13], CRA_Score_Order)df$Int_Commitment <- as.integer(df$Commitment.to.Learning)
df$Int_Interpersonal <- as.integer(df$Interpersonal.Skills)
df$Int_Communication <- as.integer(df$Communication.Skills)
df$Int_Time <- as.integer(df$Effective.Use.of.Time.and.Resources)
df$Int_Feedback <- as.integer(df$Use.of.Constructive.Feedback)
df$Int_Solving <- as.integer(df$Problem.Solving)
df$Int_Professionalism <- as.integer(df$Professionalism)
df$Int_Responsibility <- as.integer(df$Responsibility)
df$Int_Thinking <- as.integer(df$Critical.Thinking)
df$Int_Stress <- as.integer(df$Stress.Management)
df<-mutate(df, Total = Int_Commitment + Int_Interpersonal + Int_Communication + Int_Time + Int_Feedback + Int_Solving + Int_Professionalism + Int_Responsibility + Int_Thinking + Int_Stress)Preliminary - exploratory
Early exploration of the characteristics of the Clinical Readiness Tool developed by the Clinical Education Team at PSU
Purpose is really to just start getting familiar with the data.
Data frame variables and summary stats
summary(df)## ID Class.Graduating.Year. Sex
## Min. : 1.00 2024:26 Female:17
## 1st Qu.: 7.25 Male : 9
## Median :13.50
## Mean :13.50
## 3rd Qu.:19.75
## Max. :26.00
## Commitment.to.Learning Interpersonal.Skills
## Not Yet at Beginner Level: 1 Not Yet at Beginner Level: 2
## Beginner Level :21 Beginner Level :22
## Developing Level : 4 Developing Level : 2
## Entry Level : 0 Entry Level : 0
##
##
## Communication.Skills Effective.Use.of.Time.and.Resources
## Not Yet at Beginner Level: 3 Not Yet at Beginner Level: 4
## Beginner Level :21 Beginner Level :19
## Developing Level : 2 Developing Level : 3
## Entry Level : 0 Entry Level : 0
##
##
## Use.of.Constructive.Feedback Problem.Solving
## Not Yet at Beginner Level: 3 Not Yet at Beginner Level: 4
## Beginner Level :23 Beginner Level :19
## Developing Level : 0 Developing Level : 3
## Entry Level : 0 Entry Level : 0
##
##
## Professionalism Responsibility
## Not Yet at Beginner Level: 2 Not Yet at Beginner Level: 2
## Beginner Level :20 Beginner Level :20
## Developing Level : 4 Developing Level : 4
## Entry Level : 0 Entry Level : 0
##
##
## Critical.Thinking Stress.Management Semester
## Not Yet at Beginner Level: 7 Not Yet at Beginner Level: 4 2:26
## Beginner Level :18 Beginner Level :22
## Developing Level : 1 Developing Level : 0
## Entry Level : 0 Entry Level : 0
##
##
## Int_Commitment Int_Interpersonal Int_Communication Int_Time
## Min. :1.000 Min. :1 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2 Median :2.000 Median :2.000
## Mean :2.115 Mean :2 Mean :1.962 Mean :1.962
## 3rd Qu.:2.000 3rd Qu.:2 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :3.000 Max. :3 Max. :3.000 Max. :3.000
## Int_Feedback Int_Solving Int_Professionalism Int_Responsibility
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :1.885 Mean :1.962 Mean :2.077 Mean :2.077
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :3.000 Max. :3.000 Max. :3.000
## Int_Thinking Int_Stress Total
## Min. :1.000 Min. :1.000 Min. :11.00
## 1st Qu.:1.250 1st Qu.:2.000 1st Qu.:19.00
## Median :2.000 Median :2.000 Median :20.00
## Mean :1.769 Mean :1.846 Mean :19.65
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:20.00
## Max. :3.000 Max. :2.000 Max. :27.00Histograms on integer variables
This overview of the distributions on all the variables isn’t working Stress and Feedback right now - it may be that those have too limited of a distribution for this approach to work. So below there are separate histograms on those two variables.
dfNumeric <- df[,15:25]hist.data.frame(dfNumeric, nclass = 4)
hist(df$Int_Stress)
hist(df$Int_Feedback)
Correlations (Spearman - nonparametric)
dfMatrix <- as.matrix(as.data.frame(lapply(dfNumeric, as.numeric)))
rcorr(dfMatrix, type = "spearman")## Int_Commitment Int_Interpersonal Int_Communication Int_Time
## Int_Commitment 1.00 0.24 0.45 0.54
## Int_Interpersonal 0.24 1.00 0.68 0.57
## Int_Communication 0.45 0.68 1.00 0.67
## Int_Time 0.54 0.57 0.67 1.00
## Int_Feedback 0.07 0.31 0.25 0.44
## Int_Solving 0.72 0.38 0.49 0.56
## Int_Professionalism 0.35 0.82 0.75 0.79
## Int_Responsibility 0.35 0.82 0.75 0.79
## Int_Thinking 0.46 0.41 0.64 0.54
## Int_Stress 0.12 0.54 0.46 0.38
## Total 0.63 0.62 0.68 0.75
## Int_Feedback Int_Solving Int_Professionalism
## Int_Commitment 0.07 0.72 0.35
## Int_Interpersonal 0.31 0.38 0.82
## Int_Communication 0.25 0.49 0.75
## Int_Time 0.44 0.56 0.79
## Int_Feedback 1.00 0.22 0.31
## Int_Solving 0.22 1.00 0.48
## Int_Professionalism 0.31 0.48 1.00
## Int_Responsibility 0.31 0.48 1.00
## Int_Thinking 0.32 0.70 0.54
## Int_Stress 0.18 0.17 0.50
## Total 0.25 0.74 0.76
## Int_Responsibility Int_Thinking Int_Stress Total
## Int_Commitment 0.35 0.46 0.12 0.63
## Int_Interpersonal 0.82 0.41 0.54 0.62
## Int_Communication 0.75 0.64 0.46 0.68
## Int_Time 0.79 0.54 0.38 0.75
## Int_Feedback 0.31 0.32 0.18 0.25
## Int_Solving 0.48 0.70 0.17 0.74
## Int_Professionalism 1.00 0.54 0.50 0.76
## Int_Responsibility 1.00 0.54 0.50 0.76
## Int_Thinking 0.54 1.00 0.23 0.81
## Int_Stress 0.50 0.23 1.00 0.50
## Total 0.76 0.81 0.50 1.00
##
## n= 26
##
##
## P
## Int_Commitment Int_Interpersonal Int_Communication Int_Time
## Int_Commitment 0.2409 0.0225 0.0044
## Int_Interpersonal 0.2409 0.0002 0.0023
## Int_Communication 0.0225 0.0002 0.0002
## Int_Time 0.0044 0.0023 0.0002
## Int_Feedback 0.7332 0.1272 0.2270 0.0232
## Int_Solving 0.0000 0.0569 0.0107 0.0027
## Int_Professionalism 0.0763 0.0000 0.0000 0.0000
## Int_Responsibility 0.0763 0.0000 0.0000 0.0000
## Int_Thinking 0.0175 0.0398 0.0004 0.0043
## Int_Stress 0.5449 0.0041 0.0194 0.0531
## Total 0.0005 0.0007 0.0001 0.0000
## Int_Feedback Int_Solving Int_Professionalism
## Int_Commitment 0.7332 0.0000 0.0763
## Int_Interpersonal 0.1272 0.0569 0.0000
## Int_Communication 0.2270 0.0107 0.0000
## Int_Time 0.0232 0.0027 0.0000
## Int_Feedback 0.2879 0.1293
## Int_Solving 0.2879 0.0127
## Int_Professionalism 0.1293 0.0127
## Int_Responsibility 0.1293 0.0127 0.0000
## Int_Thinking 0.1126 0.0000 0.0045
## Int_Stress 0.3798 0.3966 0.0089
## Total 0.2207 0.0000 0.0000
## Int_Responsibility Int_Thinking Int_Stress Total
## Int_Commitment 0.0763 0.0175 0.5449 0.0005
## Int_Interpersonal 0.0000 0.0398 0.0041 0.0007
## Int_Communication 0.0000 0.0004 0.0194 0.0001
## Int_Time 0.0000 0.0043 0.0531 0.0000
## Int_Feedback 0.1293 0.1126 0.3798 0.2207
## Int_Solving 0.0127 0.0000 0.3966 0.0000
## Int_Professionalism 0.0000 0.0045 0.0089 0.0000
## Int_Responsibility 0.0045 0.0089 0.0000
## Int_Thinking 0.0045 0.2599 0.0000
## Int_Stress 0.0089 0.2599 0.0095
## Total 0.0000 0.0000 0.0095d<-cor(dfNumeric)
corrplot(d, method = 'ellipse', type = 'upper')
Total score
hist(df$Total)
Total score related to sex
SexBoxPlot <- ggplot(data = df, aes(x=Sex, y=Total)) +
geom_boxplot()
SexBoxPlot
Some options that might be interesting
I did not do this because it would require a bit more work go get this data
Add variables to the data:
- Age
- GPA
- Interview Score from admissions process
- Overall review score from admissions process