Rules of Engagement: Residents’ Perceptions of the In-Training Evaluation Process
Bibliographic record
Abstract
BACKGROUND: In-training evaluation reports (ITERs) often fall short of their goals of promoting resident learning and development. Efforts to address this problem through faculty development and assessment-instrument modification have been disappointing. The authors explored residents' experiences and perceptions of the ITER process to gain insight into why the process succeeds or fails. METHOD: Using a grounded theory approach, semistructured interviews were conducted with 20 residents. Constant comparative analysis for emergent themes was conducted. RESULTS: All residents identified aspects of "engagement" in the ITER process as the dominant influence on the success of ITERs. Both external (evaluator-driven, such as evaluator credibility) and internal (resident-driven, such as self-assessment) influences on engagement were elaborated. When engagement was lacking, residents viewed the ITER process as inauthentic. CONCLUSIONS: Engagement is a critical factor to consider when seeking to improve ITER use. Our articulation of external and internal influences on engagement provides a starting point for targeted interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".