Discrepancies Between Preceptor and Resident Performance Assessment: Using an Electronic Formative Assessment Tool to Improve Residents’ Self-Assessment Skills
Bibliographic record
Abstract
PROBLEM: Accurate self-assessment is a critical skill for residents to develop to become safe, adaptive clinicians upon graduation. Physicians need to be able to identify and fill in knowledge and skill gaps to deal with the rapid expansion of medical knowledge and unpredicted novel emerging medical issues. Residency training to date has not consistently focused on building these overarching skills, nor have the burgeoning assessment data that competency-based medical education (CBME) affords been used beyond their initial intent to inform summative assessment decisions. Both are important missed opportunities. APPROACH: The Queen's University Family Medicine Program adopted CBME in 2010. In 2011, it added the capacity for residents to electronically self-assess their daily performance, with preceptors reviewing and modifying as needed before submitting. In 2018, it designed software to report discordance between residents' self-assessment and preceptors' assessment of performance. OUTCOMES: From 2011-2019, 56,585 field notes were submitted, 11,429 by residents, with 28% of those (3,200/11,429) showing discordance between residents' and preceptors' performance assessments. When discordant, residents assessed their performance as less competent (undercalled) than their preceptor did 73% of the time (2,336/3,200 field notes). For the 864 field notes (27% of 3,200 discordant notes) where residents rated their performance higher than their preceptor did (overcalled, for 162/1,120 [14%] residents), 6 residents overcalled performance to a dangerous extent (2 or 3 levels of supervision higher than what their supervisors assessed them at) and 26 repeatedly (greater than 5 times) overcalled their level of performance by 1 supervisory level. NEXT STEPS: Inaccurate self-assessment (both overcalling and undercalling performance) has negative consequences. Awareness is a first step in addressing this. Discrepancy reports will be used during regular academic reviews with residents to discuss the nature, degree, and frequency of discrepancies, with the intent of fostering improved self-assessment of performance.
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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 | 0.038 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".