Feasibility of Physician Peer Assessment in an Academic Health Sciences Centre
Bibliographic record
Abstract
Peer assessment has become an important component of physician evaluation. In an academic health sciences centre, in addition to clinical care there is a significant focus on education, training and research. The literature suggests that the use of a 360-degree evaluation can provide physicians with valuable information on many aspects of their practice and can inform both professional and personal development. We conducted a pilot study to determine the feasibility of using peer assessment as part of the evaluation of our academic physicians. To maintain anonymity, an outside company was engaged to conduct the study. Participants completed a self-assessment and provided the names of eight physician peers and eight non-physician peers who were then requested to complete an evaluation. In addition, 25 patients were asked to provide their feedback. All questionnaires were forwarded directly to the outside company, which then compiled the data and provided each participant with a final report. Results indicate that it is feasible to carry out peer assessment within an academic health sciences centre. Participants noted the value of the process for career development and quality improvement.
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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 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".