Factors influencing family physician engagement in practice-based quality improvement
Bibliographic record
Abstract
OBJECTIVE: To explore the experiences of family physicians leading quality improvement (QI) efforts and to better understand facilitators and barriers related to advancing QI in family practice. DESIGN: Qualitative descriptive study. SETTING: The Department of Family and Community Medicine at the University of Toronto in Ontario. The department launched a quality and innovation program in 2011 with the dual goals of teaching QI skills to learners and supporting faculty in leading QI efforts in practice. PARTICIPANTS: Family physician faculty who held QI leadership roles at any of the department's 14 teaching units between 2011 and 2018. METHODS: Fifteen semistructured telephone interviews were conducted over 3 months in 2018. Analysis was informed by a qualitative descriptive approach. Consistency of responses across the interviews was suggestive of thematic saturation. MAIN FINDINGS: Substantial variation was found in the level of engagement with QI in practice settings despite the common training, forms of support, and curriculum the department provided. Four factors influenced the uptake of QI. First, committed leadership across the organization was fundamental to developing an effective QI culture. Second, external drivers such as mandatory QI plans sometimes motivated engagement in QI but sometimes were barriers, particularly when internal priorities conflicted with external demands. Third, at many practices, QI was widely perceived as extra work rather than as a way to enable better patient care. Finally, physicians described lack of time and resources as a challenge, particularly in community practices, and advocated for practice facilitation as a mechanism to support QI efforts. CONCLUSION: Advancing QI in primary care practice will require committed leaders, a clear understanding among physicians of the potential benefits of QI, alignment of external demands with internal drivers for improvement, and dedicated time for QI work along with support such as practice facilitation.
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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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".