FACTORS OF A PHYSICIAN QUALITY IMPROVEMENT LEADERSHIP COALITION THAT INFLUENCE PHYSICIAN BEHAVIOUR
Bibliographic record
Abstract
This manuscript-style thesis investigated the Strategic Clinical Improvement Committee (SCIC), a physician-led coalition that developed a provincial laboratory-test ordering overuse (LTOO) initiative aimed at reducing blood urea nitrogen (BUN) test ordering across hospital medicine (MED) units and emergency departments (ED) in Alberta, Canada. Two studies in three separate manuscripts contributed to the mixed methods aim of identifying coalition factors that enable MED and ED physicians to lead, participate in, and influence appropriate BUN test ordering. Manuscript 1 is a scoping review; it resulted in a synthesis of 11 articles representing nine distinct physician-led approaches that incorporate learning the science of improvement. From these, 20 enabler strategies were described, which were grouped to generate eight overarching themes that may enable physician quality improvement (QI) capability, participation, and leadership. Manuscript 2 is a qualitative exploration of the physician experience; interviews with 12 physicians from seven participating hospitals generated textual data. A content analysis was completed that identified nine overarching themes and 11 change techniques that may encourage physician QI involvement and appropriate laboratory test ordering. Manuscript 3 is a combination of the quantitative (total monthly BUN test data for six participating hospitals) and qualitative findings. BUN testing was reduced significantly in five of six hospitals and resulted in cost avoidance. Physicians had similar perceptions of the characteristics that enabled their QI involvement, which included a simple initiative linked to a coalition physician leader and/or member, credibility, mentorship, support personnel, QI education and hands-on training, minimal physician effort, and no clinical workflow disruption. Implementing person- and system-focused intervention components, and communication from a trusted local physician were factors influencing appropriate BUN test ordering. The SCIC was found to be an effective physician QI engagement strategy. Results from these studies deepen understanding of the behavioural characteristics and strategies that motivate physician behaviour for QI involvement and appropriate BUN test ordering, reducing LTOO. Researchers, policymakers, physicians, and health organization leaders may use these findings to establish, deliver, and promote physician-led QI beyond a single context.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".