Co-Worker Assessment and Physician Multisource Feedback
Bibliographic record
Abstract
Background: Multisource feedback (MSF) is increasingly being used as one of the components in revalidation and recertification processes to guide physicians' continuing professional development.Data provided by coworkers (e.g., nurses, pharmacists, technicians) are recognized as integral for assessing a physician's communication, teamwork and interprofessional abilities.The purpose of this study was to examine both the reliability of co-worker scores and the association between co-worker familiarity and physician ratings as both affect perceptions of the quality of feedback and the likelihood that recipients will take their feedback seriously.Method: MSF data from 9674 co-workers of 1341 Alberta physicians across 9 specialty groups were analyzed.Analyses for internal consistency and generalizability theory (G and D-studies) were used to assess reliability.The association between co-worker familiarity and the MSF scores they provided to physicians was assessed using ANOVA.Results: Cronbach's alpha for all co-worker tools was > 0.90.Generalizability coefficients (EP 2 ) varied by specialty and ranged from 0.56 to 0.72.D studies revealed that a minimum of 11 co-workers are necessary to achieve stability (i.e., EP 2 > 0.70).Co-worker familiarity exerted a significant (p < .001)positive main effect on physician performance scores, across all specialty groupings. ConclusionsThis study confirms the reliability of co-worker scores and provides evidence that co-worker MSF data is stable and consistent for the purposes of providing physicians with feedback for professional development.Attention however needs to be paid to co-worker/physician familiarity as this relationship may favourably bias physician performance scores.
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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 | 0.021 | 0.166 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".