Assessing Change in Clinical Teaching Skills: Are We Up for the Challenge?
Bibliographic record
Abstract
BACKGROUND: The faculty development community has been challenged to more rigorously assess program impact and move beyond traditional outcomes of knowledge tests and self ratings. PURPOSE: The purpose was to (a) assess our ability to measure supervisors' feedback skills as demonstrated in a clinical setting and (b) compare the results with traditional outcome measures of faculty development interventions. METHODS: A pre-post study design was used. Resident and expert ratings of supervisors' demonstrated feedback skills were compared with traditional outcomes, including a knowledge test and participant self-evaluation. RESULTS: Pre-post knowledge increased significantly (pre = 61%, post = 85%; p < .001) as did participant's self-evaluation scores (pre = 4.13, post = 4.79; p < .001). Participants' self-evaluations were moderately to poorly correlated with resident (pre r = .20, post r = .08) and expert ratings (pre r = .43, post r = -.52). Residents and experts would need to evaluate 110 and 200 participants, respectively, to reach significance. CONCLUSIONS: It is possible to measure feedback skills in a clinical setting. Although traditional outcome measures show a significant effect, demonstrating change in teaching behaviors used in practice will require larger scale studies than typically undertaken currently.
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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 | 0.126 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".