Defining the capabilities and competencies of high-performing family physicians: a mixed methods study
Bibliographic record
Abstract
INTRODUCTION: High-performing primary care is recognised as the foundation of an effective and efficient healthcare system. Many medical graduates report they are not prepared for independent practice. To date, no research has been conducted to identify the key capabilities and competencies of high-performing family medicine graduates in Canada. This pilot project aims to identify the capabilities and competencies of high-performing early-career family physicians in Ontario, Canada, and explore opportunities for enhancing learning, teaching and assessment within family medicine residency programmes. METHODS AND ANALYSIS: Employing a mixed-methods explanatory sequential study design, this research will use a theory-driven Professional Capability Framework, previously validated in studies across nine professions, to guide the investigation. The first (quantitative) phase involves surveying ~50 high-performing early-career family physicians identified as high performing by educators, colleagues and leaders. The objective of the survey is to identify the key competencies and personal, interpersonal and cognitive capabilities of high-performing family physicians. The second (qualitative) phase involves conducting workshops with stakeholders, including educators, professional associations, regulators and colleges, to test the veracity of the results. Quantitative data will be analysed using descriptive statistics, and qualitative data will be analysed using Braun and Clarke's thematic analysis. The first and second phases will identify the key capabilities and competencies required to confidently adapt to the independent practice of comprehensive family medicine and inform fit-for-purpose educational strategies for teaching, learning and assessment. ETHICS AND DISSEMINATION: The study is approved by the University of Toronto's Health Sciences Research Ethics Board (#41799). Research findings will be discussed with professional bodies, educators responsible for family medicine curricula and universities. Study findings will also be disseminated through academic conferences and academic publications in peer-reviewed journals. Project summaries and infographics will be developed and disseminated to key stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".