Unpacking the intention to action gap: a qualitative study understanding how physicians engage with audit and feedback
Bibliographic record
Abstract
BACKGROUND: Audit and feedback (A&F) often successfully enhances health professionals' intentions to improve quality of care but does not consistently lead to practice changes. Recipients often cite data credibility and limited resources as barriers impeding their ability to act upon A&F, suggesting the intention-to-action gap manifests while recipients are interacting with their data. While attention has been paid to the role feedback and contextual variables play in contributing to (or impeding) success, we lack a nuanced understanding of how healthcare professionals interact with and process clinical performance data. METHODS: We used qualitative, semi-structured interviews guided by Normalization Process Theory (NPT). Questions explored the role of data in quality improvement, experiences with the A&F report, perceptions of the data, and interpretations and reflections. Interviews were audio-recorded and transcribed verbatim. Data were analyzed using a combination of inductive and deductive strategies using reflexive thematic analysis informed by a constructivist paradigm. RESULTS: Healthcare professional characteristics (individual quality improvement capabilities and beliefs about data) seem to influence engagement with A&F to a greater degree than feedback variables (i.e., delivered by peers) and observed contextual factors (i.e., strong quality improvement culture). Most participants lacked the capabilities to interpret practice-level data in an actionable way despite a motivation to engage meaningfully. Reasons for the intention-to-action gap included challenges interpreting longitudinal data, appreciating the nuances of common data sources, understanding how aggregate data provides insights into individualized care, and identifying practice-level actions to improve quality. These factors limited effective cognitive participation and collective action, as outlined in NPT. CONCLUSIONS: A well-designed A&F intervention is necessary but not sufficient to inform practice changes. A&F initiatives must include co-interventions to address recipient characteristics (i.e., beliefs and capabilities) and context to optimize impact. Effective strategies to overcome the intention-to-action gap may include modelling how to use A&F to inform practice change, providing opportunities for social interaction relating to the A&F, and circulating examples of effective actions taken in response to A&F. More broadly, undergraduate medical education and post-graduate training must ensure physicians are equipped with QI capabilities, with an emphasis on the skills required to interpret and act on practice-level data.
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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 | 0.042 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".