Factors of a physician quality improvement leadership coalition that influence physician behaviour: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: A coalition (Strategic Clinical Improvement Committee), with a mandate to promote physician quality improvement (QI) involvement, identified hospital laboratory test overuse as a priority. The coalition developed and supported the spread of a multicomponent initiative about reducing repetitive laboratory testing and blood urea nitrogen (BUN) ordering across one Canadian province. This study's purpose was to identify coalition factors enabling medicine and emergency department (ED) physicians to lead, participate and influence appropriate BUN test ordering. METHODS: Using sequential explanatory mixed methods, intervention components were grouped as person focused or system focused. Quantitative phase/analyses included: monthly total and average of the BUN test for six hospitals (medicine programme and two EDs) were compared pre initiative and post initiative; a cost avoidance calculation and an interrupted time series analysis were performed (participants were divided into two groups: high (>50%) and low (<50%) BUN test reduction based on these findings). Qualitative phase/analyses included: structured virtual interviews with 12 physicians/participants; a content analysis aligned to the Theoretical Domains Framework and the Behaviour Change Wheel. Quotes from participants representing high and low groups were integrated into a joint display. RESULTS: Monthly BUN test ordering was significantly reduced in 5 of 6 participating hospital medicine programmes and in both EDs (33% to 76%), resulting in monthly cost avoidance (CAN$900-CAN$7285). Physicians had similar perceptions of the coalition's characteristics enabling their QI involvement and the factors influencing BUN test reduction. CONCLUSIONS: To enable physician confidence to lead and participate, the coalition used the following: a simply designed QI initiative, partnership with a coalition physician leader and/or member; credibility and mentorship; support personnel; QI education and hands-on training; minimal physician effort; and no clinical workflow disruption. Implementing person-focused and system-focused intervention components, and communication from a trusted local physician-who shared data, physician QI initiative role/contribution and responsibility, best practices, and past project successes-were factors influencing appropriate BUN test ordering.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".