Relying on Others’ Reliability: Challenges in Clinical Teaching Assessment
Bibliographic record
Abstract
BACKGROUND: The quality of the data generated from internally created faculty teaching instruments often draws skepticism. Strategies aimed at improving the reliability and validity of faculty teaching assessments tend to revolve around literature searches for a replacement instrument(s). PURPOSE: The purpose was to test this "search-and-apply" method and discuss our experiences with it within the context of observational assessment practice. METHOD: In a naturalistic pilot test, two previously validated faculty assessment instruments were paired with a global question. The reliability of both metrics was estimated. RESULTS: Generalizability analyses indicated that for both pilot tested faculty teaching instruments, the global question was a more reliable measure of perceived clinical teaching effectiveness than a multiple-item inventory. Item analysis with Cronbach's coefficient alpha suggested redundant instrument content. Rater error accounted for the greatest proportion of the variance and straight-line responses occurred in approximately 28% of residents' appraisals. CONCLUSIONS: The results of the present study draw attention to one of the common fallacies surrounding instrument-based assessment in medical education; the solution to improving one's assessment practice primarily involves identifying a previously published instrument from the literature. Academic centers need to invest in ongoing quality control efforts including the pilot testing of any proposed instruments.
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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 | 0.556 | 0.788 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".