Understanding Emotions Impacted by New Assessment Mandates Implemented in Medical Education: A Survey of Residents and Faculty Across Multiple Specialties
Bibliographic record
Abstract
Background Previous research findings show that the overall perception of residents regarding the new entrustable professional activity (EPA) assessment mandates is primarily negative. Hence, this study aims to explore the link between EPA assessment experiences and resident and faculty emotions and expectancy of successfully completing residency training. Methods A standardized questionnaire (Medical Emotions Scale (MES)), which measures 20 unique emotions on a 5-point Likert scale, was used to explore the emotions of residents and faculty members regarding EPA assessments and residents' expectancy of success. Data analysis included descriptive statistics and analysis of variance (ANOVA). Results Ninety-one (N=91) participants (46 faculty members and 45 residents) completed the survey. The results revealed that residents have more negative emotions toward EPA assessments compared to faculty. Additionally, resident and faculty emotions regarding EPA assessments vary across specialty and gender. Conclusions These findings will be crucial in providing the Royal College of Physicians and Surgeons of Canada and medical education programs with concrete evidence and guidance in understanding the perspectives and emotions of residents and faculty towards EPA assessments and residents' beliefs about successfully completing their medical training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".