Quality evaluation reports: Can a faculty development program make a difference?
Bibliographic record
Abstract
BACKGROUND: The quality of medical student and resident clinical evaluation reports submitted by rotation supervisors is a concern. The effectiveness of faculty development (FD) interventions in changing report quality is uncertain. AIMS: This study assessed whether faculty could be trained to complete higher quality reports. METHOD: A 3-h interactive program designed to improve evaluation report quality, previously developed and tested locally, was offered at three different Canadian medical schools. To assess for a change in report quality, three reports completed by each supervisor prior to the workshop and all reports completed for 6 months following the workshop were evaluated by three blinded, independent raters using the Completed Clinical Evaluation Report Rating (CCERR): a validated scale that assesses report quality. RESULTS: A total of 22 supervisors from multiple specialties participated. The mean CCERR score for reports completed after the workshop was significantly higher (21.74 ± 4.91 versus 18.90 ± 5.00, p = 0.02). CONCLUSIONS: This study demonstrates that this FD workshop had a positive impact upon the quality of the participants' evaluation reports suggesting that faculty have the potential to be trained with regards to trainee assessment. This adds to the literature which suggests that FD is an important component in improving assessment quality.
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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 | 0.225 | 0.481 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".