The engagement equation: a model for understanding what drives voluntary physician engagement with data-driven clinical performance feedback
Bibliographic record
Abstract
BACKGROUND: Clinical performance feedback (CPF) is widely used to support physician development and improve care. Yet, its impact remains limited by low voluntary engagement. This study sought to: (1) develop a theory-informed, report-agnostic model outlining the key beliefs that shape physician engagement with CPF; (2) explore patterns of feedback orientation across physicians; and (3) understand how individual perceptions influence engagement with CPF. METHODS: We used a cross-sectional, multi-method approach combining a survey and qualitative interviews with primary care physicians in Ontario, Canada. We validated a conceptual model using path analysis, explored heterogeneity in feedback orientation using latent profile analysis, and qualitatively examined how perceptions of CPF influenced engagement. RESULTS: Survey results (n = 206) supported a model in which engagement with CPF is shaped by five recipient characteristics: perceived need for change (change discrepancy), perceived value of CPF, confidence to act on feedback (feedback self-efficacy), belief that feedback is useful (feedback utility), and sense of responsibility to act (feedback accountability). Perceived utility mediated the effects of self-efficacy and value on accountability, and perceived need for change influenced value. Latent profile analysis identified three groups: physicians with high and balanced feedback orientation (n = 32), moderate and balanced (n = 143), and low feedback orientation with low self-efficacy (n = 31). Interview findings (n = 9) revealed two mindsets: physicians who saw value in CPF despite its limitations (engagers), and those who dismissed its relevance (non-engagers). These mindsets aligned with differences in value, utility, and accountability scores from the survey. CONCLUSIONS: Engagement with CPF is not one-size-fits-all. Physicians differ in how they appraise and act on feedback based on their beliefs about its relevance, usefulness, and their ability to act. CPF initiatives should explicitly link feedback to improved patient outcomes, focus on future actions, and provide clear, actionable guidance. Designing CPF that accounts for recipient heterogeneity is essential to realizing its full potential as an improvement strategy.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".