Enabling positive practice improvement through <scp>data‐driven</scp> feedback: A model for understanding how data and self‐perception lead to practice change
Bibliographic record
Abstract
PURPOSE: This article aims to identify the factors that affect physicians' experiences of receiving practice data and to use these data to develop a model describing how individuals interact with the data. METHODS: We designed an interview guide to study physicians' perspectives on audit and feedback. By intentional sampling, we recruited 15 physicians amongst gender groups, types of practice (academic vs community), and durations of practice. The interviews were conducted by a single author and transcribed without identifiers. We then began with an open coding analysis for all of the transcripts, and thereafter conducted axial coding to group the data into larger themes. RESULTS: Several attributes were identified as either enabling or counterproductive attributes for participant improvement. The final proposed model identifies different zones of engagement on the basis of both the individual practitioner's growth mindset and the quality of the existing data system. In the highest engagement zone, the mindset of the collective leadership is one of growth. Systemic supports are in place, which potentiates learning that may come from an individual motivated to use their own data. CONCLUSION: Our novel model depicts the relationship between data feedback systems and individuals' mindsets interact to augment or hinder clinical practice improvement. This model may provide leaders with a framework to examine their academic and administrative structures and how they might interface with performance feedback systems with clinicians.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".