Factors influencing practice choices of early-career family physicians: A qualitative interview study
Bibliographic record
Abstract
Context: Despite an increasing number of family physicians per capita, Canadians often report challenges in accessing primary care. This has caused speculation about whether new family physicians are practicing differently than their predecessors. Objective: To describe the practice characteristics of early career family physicians and the factors that influenced their practice choices. Study Design: Qualitative interview study and main qualitative arm of the broader mixed-methods ECPC study. Setting: Canadian family medicine in British Columbia, Ontario, and Nova Scotia. Population studied: Family physicians in their first 10 years of practice. Results: 63 family physicians were purposefully sampled for maximum variation and then interviewed. Interview transcripts were analyzed using framework analysis. Within our sample, 38% of physicians worked solely in family practice, 11% solely in focused practice, and 51% combined both, spending part of their time in clinic-based family practice and part within a focused practice. Many also chose to work across numerous settings, including short- and long-term locums, filling in where there were service needs. Influences affecting practice choices (e.g., family medicine clinic vs. focused practice) were identified and included training (primarily residency), professional considerations (e.g., practice model and payment policies), and personal factors (e.g., family responsibilities). Practice characteristics most broadly susceptible to these influences were scope of practice, practice type or model, and practice location. Other characteristics such as schedule and work volume were more narrowly influenced, typically by family considerations. Conclusions: Practice choices were influenced by a mosaic of factors, some of which are beyond the reach of policymakers but need to be acknowledged in workforce planning. Other influential factors present opportunities for changes in physician training and practice-related policy to help reshape primary care provision and ensure patients can access the services they need.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".