Family Physician Quality Improvement Plans: A Realist Inquiry Into What Works, for Whom, Under What Circumstances
Bibliographic record
Abstract
INTRODUCTION: Evaluation of quality improvement programs shows variable impact on physician performance often neglecting to examine how implementation varies across contexts and mechanisms that affect uptake. Realist evaluation enables the generation, refinement, and testing theories of change by unpacking what works for whom under what circumstances and why. This study used realist methods to explore relationships between outcomes, mechanisms (resources and reasoning), and context factors of a national multisource feedback (MSF) program. METHODS: Linked data for 50 physicians were examined to determine relationships between action plan completion status (outcomes), MSF ratings, MSF comments and prescribing data (resource mechanisms), a report summarizing the conversation between a facilitator and physician (reasoning mechanism), and practice risk factors (context). Working backward from outcomes enabled exploration of similarities and differences in mechanisms and context. RESULTS: The derived model showed that the completion status of plans was influenced by interaction of resource and reasoning mechanisms with context mediating the relationships. Two patterns were emerged. Physicians who implemented all their plans within six months received feedback with consistent messaging, reviewed data ahead of facilitation, coconstructed plan(s) with the facilitator, and had fewer risks to competence (dyscompetence). Physicians who were unable to implement any plans had data with fewer repeated messages and did not incorporate these into plans, had difficult plans, or needed to involve others and were physician-led, and were at higher risk for dyscompetence. DISCUSSION: Evaluation of quality improvement initiatives should examine program outcomes taking into consideration the interplay of resources, reasoning, and risk factors for dyscompetence.
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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 | 0.061 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".