Who chooses to engage? Understanding the characteristics of physicians who voluntarily engage with an audit and feedback intervention: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Voluntary clinician engagement in audit and feedback (A&F) initiatives is often suboptimal. We compared a range of characteristics between physicians who voluntarily engaged and those who did not. METHODS: We conducted a retrospective cohort study using administrative databases and a survey. The cohort included Ontario primary care physicians practising between April 2017 and March 2020. The A&F group voluntarily enrolled in an A&F intervention; control physicians did not enrol. We compared demographic, practice and patient characteristics between groups. Four validated questionnaires measured beliefs about feedback and improvement. Logistic regression modelled adjusted odds of A&F engagement. RESULTS: Of 10 654 active primary care physicians in Ontario, 1400 voluntarily engaged in A&F compared with 9254 controls. Physicians had higher odds of engaging with A&F if they had a larger roster size (OR (95% CI) 2.75 (2.15 to 3.51), p<0.001) and worked in home care (1.75 (1.55 to 1.97), p<0.001), and lower odds of engaging if they had higher continuity of care (0.66 (0.49 to 0.90), p=0.008), practised for more years (0.53 (0.44 to 0.62), p<0.001) and enrolled in a fee-for-service model (0.74 (0.65 to 0.85), p<0.001). Physicians engaged with A&F had fewer racialised or immigrant patients (25% vs 30% in marginalisation quintile 5, standardised mean difference=0.10), while other demographic characteristics were similar. Among the survey respondents (36 A&F physicians and 90 controls), higher principal support (2.28 (1.29 to 4.48), p=0.008), lower perceived need for change (discrepancy) (0.39 (0.20 to 0.70), p=0.003) and lower engagement in QI (0.43 (0.18 to 0.88), p=0.032) predicted A&F engagement. CONCLUSION: To expand A&F's reach and impact, future efforts should (1) tailor outreach to physicians with larger, more diverse practice populations, (2) seek to understand why physicians with higher perceived need for change engage less, (3) understand which physicians are most likely to benefit and (4) co-design how to make A&F more relevant and actionable for those serving marginalised communities.
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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 | 0.014 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".