Interactive peer review: an innovative resident evaluation tool.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We designed an interactive peer review process for our inpatient family practice residents using a faculty-facilitated group format. This paper describes and evaluates the method. METHODS: During inpatient rotations, first-year residents evaluate second- and third-year residents, second-year residents evaluate first- and third-year residents, and third-year residents evaluate first- and second-year residents. Evaluations are conducted in discussion format, led by a faculty facilitator. Results are shared with the resident being evaluated. We surveyed residents and faculty regarding the usefulness of this review method and their comfort with the process using a 15-question survey. RESULTS: A total of 90% of residents and 100% of faculty responded to the survey; 82% of residents and 100% of faculty felt that the peer-review process was useful. All faculty felt that peer comments correlated well with their own impressions of resident performance. Only 4% of residents felt uncomfortable knowing that peers were evaluating their performance, and 93% of residents and 100% of faculty felt that the peer-review process had supported the team environment. CONCLUSIONS: Interactive peer review is an excellent tool to obtain timely, specific, and useful information regarding resident performance and has been well accepted in our program.
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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 | 0.031 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".