Unpacking the Black Box of Improvement: Performance Feedback Orientation Among Physicians
Bibliographic record
Abstract
Audit and Feedback (A&F) is a widely utilized and promising solution to reduce unwarranted physician-level variation in care, yet its impact is variable in practice and uptake is often suboptimal. While recipient characteristics are acknowledged as an important driver of interaction with A&F reports, surprisingly little attention has been paid to understanding these characteristics and their mechanisms of action. From a control-value perspective, we developed and tested an integrated theoretical model delineating the modifiable antecedents to engaging with A&F by measuring recipient attitudes, beliefs, and regulatory focus in the healthcare context. By using a partial least squares path modeling technique, we found that 1) recipients’ commitment to act on feedback is influenced by both their perceived value of and perceived confidence in acting on feedback; 2) both pathways of influence are mediated by perceived usefulness of feedback; 3) recipients’ perceived need for change influences their perceived value of acting on feedback; and 4) prevention focus has a negative but insignificant association with perceived usefulness of feedback. Our findings contribute to a more in-depth understanding of the upstream cognitive appraisal process of A&F, which determines recipients’ intention to interact with A&F.
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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 | 0.022 | 0.076 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".