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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".