Survey of physician attitudes to using multisource feedback for competence assessment in Alberta
Bibliographic record
Abstract
BACKGROUND: The use of multisource feedback (MSF) for assessing physician performance is widespread and rapidly growing. Findings from early very small research studies using highly selected participants suggest high levels of satisfaction and support. However, after nearly two decades of experience using MSF to evaluate all physicians in Alberta, we are sceptical of this. OBJECTIVES: To determine physicians' actual opinions of MSF using the entire physician population of Alberta, Canada DESIGN: Online survey. SETTING: Alberta, Canada. PARTICIPANTS: All physicians with a full licence to practice in Alberta in 2015. INTERVENTIONS: All participants were asked to grade how well they thought MSF was at assessing various aspects of physician performance using a 10-point Likert-type scale. There was also a text response field for written comments. OUTCOMES: Mean responses to quantitative questions. Qualitative content and thematic analysis of open-ended text responses.We analysed the data using SPSS V.23 and NVivo V.11 and built a multivariate model highlighting the predictors of high and low opinions of MSF. RESULTS: Survey response rate was high for physicians with 2215 responses (25%). The mean rating for how successful MSF was at assessing a variety of dimensions, varied from a low of 5.03/10 for medical knowledge to a high of 6.38/10 for professionalism and communication. Canadian-trained MDs rated MSF significantly lower on every dimension by approximately 20% compared with non-Canadian-trained MDs. CONCLUSIONS: Alberta physicians have much lower opinions about the ability of MSF to measure any dimension of their performance than what has been suggested in the literature. Canadian-trained MDs have a particularly low opinion of MSF for reasons that remain unclear. The results of this survey offer a serious challenge to the effectiveness of a programme that is designed to promote self-reflection and performance 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".