Beyond quality improvement: exploring why primary care teams engage in a voluntary audit and feedback program
Bibliographic record
Abstract
Abstract Background Despite its popularity, the effectiveness of audit and feedback in support quality improvement efforts is mixed. While audit and feedback-related research efforts have investigated issues relating to feedback design and delivery, little attention has been directed towards factors which motivate interest and engagement with feedback interventions. This study explored the motivating factors that drove primary care teams to participate in a voluntary audit and feedback initiative. Methods Interviews were conducted with leaders of primary care teams who had participated in at least one iteration of the audit and feedback program. This intervention was developed by an organization which advocates for high-quality, team-based primary care in Ontario, Canada. Interview transcripts were coded using the Consolidated Framework for Implementation Research and the resulting framework was analyzed inductively to generate key themes. Results Interviews were completed with 25 individuals from 18 primary care teams across Ontario. The majority were Executive Directors (14), Physician leaders (3) and support staff for Quality Improvement (4). A range of motivations for participating in the audit and feedback program beyond quality improvement were emphasized. Primarily, informants believed that the program would eventually become a best-in-class audit and feedback initiative. This reflected concerns regarding existing initiatives in terms of the intervention components and intentions as well as the perception that an initiative by primary care, for primary care would better reflect their own goals and better support desired patient outcomes. Key enablers included perceived obligations to engage and provision of support for the work involved. No teams cited an evidence base for A&F as a motivating factor for participation. Conclusions A range of motivating factors, beyond quality improvement, contributed to participation in the audit and feedback program. Findings from this study highlight that efforts to understand how and when the intervention works best cannot be limited to factors within developersâ control. Clinical teams may more readily engage with initiatives with the potential to address their own long-term system goals. Aligning motivations for participation with the goals of the audit and feedback initiative may facilitate both engagement and impact.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.085 | 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".