Advice to future family physicians: findings from qualitative interviews with family medicine residents and early-career family physicians
Bibliographic record
Abstract
BACKGROUND: Canadians continue to report challenges accessing primary care. Practice choices made by primary care providers shape services available to Canadians. Although there is literature observing family medicine practice trends, there is less clarity on the reasoning underlying primary care providers' practice intentions. Advice offered by residents and early-career family physicians may reveal challenges they have experienced, how they have adapted to them, and strategies for new residents. In this paper, we examine advice family medicine residents and early-career family physicians would give to new family medicine residents. METHODS: Sixty early-career family physicians and thirty residents were interviewed as part of a mixed-methods study of practice patterns of family medicine providers in Canada. During qualitative interviews, participants were asked, "what advice would you give [a new family medicine resident] about planning their career as a family physician?" We inductively analyzed responses to this question. RESULTS: Advice consisted of understanding the current climate of family medicine (need for specialization, business management burden, physician burnout) and revealed reasons behind said challenges (lack of support for comprehensive clinic care, practical limitations of different practice models, and how payment models influence work-life balance). Subtheme analyses showed early-career family physicians being more vocal on understanding practical aspects of the field including practice logistics and achieving job security. CONCLUSION: Most advice mirrored current changes and challenges as well as revealing strategies on how primary care providers are handling the realities of practicing family medicine. Multi-modal systemic interventions may be needed to support family physicians throughout the changing reality of family medicine and ensure family medicine is an appealing specialty.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".