Bibliographic record
Abstract
PURPOSE: Although academic centers rely on assessments from medical trainees regarding the effectiveness of their faculty as teachers, little is known about how trainees conceptualize and approach their role as assessors of their clinical supervisors. METHOD: In 2010, using a constructivist grounded theory approach, five focus group interviews were conducted with 19 residents from an internal medicine residency program. A constant comparative analysis of emergent themes was conducted. RESULTS: Residents viewed clinical teaching assessment (CTA) as a time-consuming task with little reward. They reported struggling throughout the academic year to meet their CTA obligations and described several shortcut strategies they used to reduce their burden. Rather than conceptualizing their assessments as a conduit for both formative and summative feedback, residents perceived CTA as useful for the surveillance of clinical supervisors at the extremes of the spectrum of teaching effectiveness. They put the most effort, including the crafting of written comments, into the CTAs of these outliers. Trainees desired greater transparency in the CTA process and were skeptical regarding the anonymity and perceived validity of their faculty appraisals. CONCLUSIONS: Individual and system-based factors conspire to influence postgraduate medical trainees' motivation for generating high-quality appraisals of clinical teaching. Academic centers need to address these factors if they want to maximize the usefulness of these assessments.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".