Likelihood of Change: A Study Assessing Surgeon Use of Multisource Feedback Data
Bibliographic record
Abstract
BACKGROUND: Multisource feedback, using questionnaire-based data from patients, coworkers, and medical colleagues, is designed to provide broad-based information about clinical performance to facilitate change. PURPOSE: To determine and explain the likelihood that surgeons would implement change following receipt of performance data. METHODS: Surgeons were surveyed to determine the likelihood they would make changes based on specific feedback about their clinical practices. RESULTS: One hundred fifty-three surgeons (76.5%) responded to the follow-up survey. There was little correlation between performance ratings provided by self or medical colleagues and the likelihood of change. A linear regression analysis indicated that 19.2% of the variance in likelihood to change could be explained by age, time spent reviewing feedback, the gap between self- and other ratings, and surgical specialty. CONCLUSION: Surgeons made few changes in practice in response to feedback data. Attention needs to be paid to methods that might increase surgeon use of performance data
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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 | 0.027 | 0.204 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".