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 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 | 0.068 | 0.181 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".