A case for feedback and monitoring assessment in competency‐based medical education
Bibliographic record
Abstract
PURPOSE: Within competency-based medical education, self-regulated learning (SRL) requires residents to leverage self-assessment and faculty feedback. We sought to investigate the potential for competency-based assessments to foster SRL by quantifying the relationship between faculty feedback and entrustment ratings as well as the congruence between faculty assessment and resident self-assessment. MATERIALS AND METHODS: We collected comments in (a) an emergency medicine objective structured clinical examination group (objective structured clinical examinations [OSCE] and emergency medicine OSCE group [EMOG]) and (b) a first-year resident multidisciplinary resuscitation "Nightmares" course assessment group (NCAG) and OSCE group (NOG). We assessed comments across five domains including Initial Assessment (IA), Diagnostic Action (DA), Therapeutic Action (TA), Communication (COM), and entrustment. Analyses included structured qualitative coding and (non)parametric and descriptive analyses. RESULTS: In the EMOG, faculty's positive comments in the entrustment domain corresponded to lower entrustment score Mean Ranks (MRs) for IA (<11.1), DA (<11.2), and entrustment (<11.6). In NOG, faculty's negative comments resulted in lower entrustment score MRs for TA (<11.8 and <10) and DA (<12.4), and positive comments resulted in higher entrustment score MRs for IA (>15.4) and COM (>17.6). In the NCAG, faculty's positive IA comments were negatively correlated with entrustment scores (ρ = -.27, P = .04). Across programs, faculty and residents made similar domain-specific comments 13% of the time. CONCLUSIONS: Minimal and inconsistent associations were found between narrative and numerical feedback. Performance monitoring accuracy and feedback should be included in assessment validation.
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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 | 0.160 | 0.348 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".