What Multisource Feedback Factors Influence Physician Self-Assessments? A Five-Year Longitudinal Study
Bibliographic record
Abstract
BACKGROUND: Multisource feedback, in which medical colleagues, patients, coworkers, and the physician involved provide data, is a tool to inform physician practice. Its impact on physicians' self-assessment through two iterations is unknown. METHOD: Data from 250 family physicians in Alberta who participated in two iterations, five years apart-1999 and 2006--allowed the authors to determine the change in self-assessment scores, using a t test. A multiple regression was used to account for the variance in the scores from the second self-assessment by the data from the multisource feedback and sociodemographics from the first iteration. RESULTS: Physicians rated themselves higher in the second iteration. The linear regression model accounted for 27.4% of the variance in the ratings at the second iteration and incorporated data from the self-assessment. CONCLUSIONS: Physician self-assessment seems driven by stable perceptions that physicians hold about themselves and that may be slow to change.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".