Exploring family physicians' reactions to multisource feedback: perceptions of credibility and usefulness
Bibliographic record
Abstract
PURPOSE: Physician performance is comprised of several domains of professional competence. Multisource feedback (MSF) or 360-degree feedback is an approach used to assess these, particularly the humanistic and relational competencies. Research studying responses to performance assessment shows that reactions vary and can influence how performance feedback is used. Improvement does not always result, especially when feedback is perceived as negative. This small qualitative study undertook preliminary exploration of physicians' reactions to MSF, and perceptions influencing these and the acceptance and use of their feedback. METHODS: We held focus groups with 15 family physicians participating in an MSF pilot study. Qualitative analyses included content and constant comparative analyses. RESULTS: Participants agreed that the purpose of MSF assessment should be to enhance practice and generally agreed with their patients' feedback. However, responses to medical colleague and co-worker feedback ranged from positive to negative. Several participants who responded negatively did not agree with their feedback nor were inclined to use it for practice improvement. Reactions were influenced by perceptions of accuracy, credibility and usefulness of feedback. Factors shaping these perceptions included: recruiting credible reviewers, ability of reviewers to make objective assessments, use of the assessment tool and specificity of the feedback. CONCLUSION: Physicians' perceptions of the MSF process and feedback can influence how and if they use the feedback for practice improvement. These findings are important, raising the concern that feedback perceived as negative and not useful will have no or negative results, and highlight questions for further study.
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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 | 0.030 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".