Practice patterns among early-career primary care (ECPC) physicians and workforce planning implications: protocol for a mixed methods study
Bibliographic record
Abstract
INTRODUCTION: Canadians report persistent problems accessing primary care despite an increasing per-capita supply of primary care physicians (PCPs). There is speculation that PCPs, especially those early in their careers, may now be working less and/or choosing to practice in focused clinical areas rather than comprehensive family medicine, but little evidence to support or refute this. The goal of this study is to inform primary care planning by: (1) identifying values and preferences shaping the practice intentions and choices of family medicine residents and early career PCPs, (2) comparing practice patterns of early-career and established PCPs to determine if changes over time reflect cohort effects (attributes unique to the most recent cohort of PCPs) or period effects (changes over time across all PCPs) and (3) integrating findings to understand the dynamics among practice intentions, practice choices and practice patterns and to identify policy implications. METHODS AND ANALYSIS: We plan a mixed-methods study in the Canadian provinces of British Columbia, Ontario and Nova Scotia. We will conduct semi-structured in-depth interviews with family medicine residents and early-career PCPs and analyse survey data collected by the College of Family Physicians of Canada. We will also analyse linked administrative health data within each province. Mixed methods integration both within the study and as an end-of-study step will inform how practice intentions, choices and patterns are interrelated and inform policy recommendations. ETHICS AND DISSEMINATION: This study was approved by the Simon Fraser University Research Ethics Board with harmonised approval from partner institutions. This study will produce a framework to understand practice choices, new measures for comparing practice patterns across jurisdictions and information necessary for planners to ensure adequate provider supply and patient access to primary care.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".