Development of a Competency Framework for Quality Improvement in Family Medicine: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to develop a comprehensive framework of quality improvement competencies for use in continuing professional development (CPD) and continuing medical education (CME) for European general practice/family medicine physicians (GPs/FDs). METHODS: The study was carried out in three phases: literature review, consensus development panels, and Delphi technique. An initial competencies framework was developed from an extensive literature review focusing on literature in English from 2000 to 2011 and addressing quality improvement competencies for general practitioners in continuous education programs. Two rounds of reviews by consensus development panels were undertaken to evaluate and make changes to the initial draft competency framework. Then two rounds of Delphi surveys were carried out in an effort to reach consensus on the domains and competencies included in the framework. Our goal was for 90% to 100% consensus. Both surveys were presented through SurveyMonkey, an online survey service, and sent by e-mail to members of the European Association for Quality and Patient Safety in General Practice/Family Medicine (EQuiP), a network organization of Wonca Europe. RESULTS: The Quality Improvement Competencies Framework was developed. It consists of a list of 35 competencies organized into the following domains: Patient Care & Safety, Effectiveness & Efficiency, Equity & Ethical Practice, Methods & Tools, Leadership & Management, and Continuing Professional Education. CONCLUSION: We believe that the framework can serve as a useful tool for identifying gaps in knowledge and skills and guiding the development of CPD and CME curricula for GPs/FDs not only in Europe but also in other regions, including the United States and Canada, on the assumption that many of the core tasks of quality improvement would be relevant across multiple contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".