Family Practice: A more balanced, not just negative narrative based on data and evidence
Bibliographic record
Abstract
Context: Concerns about family practice are well-founded, but also negatively biased. This can fuel poor impressions among medical students and dispirit practicing family physicians. Objective: Give a balanced description of family medicine and family practice, including positive evidence that is lacking in the public narrative. Study Design and Analysis: Descriptive analysis. Pan-Canadian results based on most recent data. Datasets: Datasets from the Association of Faculties of Medicine of Canada (AFMC), Canadian Resident Matching Service (CaRMS), Canadian Institute for Health Information (CIHI), Canadian Medical Association (CMA), and College of Family Physicians of Canada (CFPC). Population Studied and Instruments: Medical students, family medicine (FM) residents, and practicing physicians in all provinces and territories. The AFMC surveys all graduating medical students in Canada (1,838 responses and response rate of 60% in 2022). CaRMS covers all residency programs in Canada (2,937 applicants in 2023). The CMA’s National Physician Health Survey (NPHS) is an open-link survey with 3,489 responses from practicing physicians in 2021. CIHI’s National Physician Database (NPDB) contains fee-for-service billing data for all physicians in Canada (269 million services in 2021). The CFPC Family Medicine Longitudinal Survey (FMLS) includes all second-year family medicine residents (53% response rate in 2022). Outcome Measures: Medical student and FM residents9 perceptions and career choices; practicing physicians9 health, satisfaction, and scope of practice. Results: 42% of graduating medical students rate their FM experiences as being “excellent”, compared to an average of 30% for other disciplines. Medical students who choose FM are highly successful in the CaRMS match; for 80% FM is their top-ranked discipline and 98% match in the first iteration. The NPHS shows that 81% of GPs have high emotional well-being, compared to 78% of medical specialists and 75% of surgical specialists. The NPDB shows that family physicians practice in broad areas; they account for 52% of all medical services, including 47% of psychotherapy counselling, 49% of hospital-based assessments, and 21% of anesthesia services. The FMLS shows that 90% of FM residents are proud to become family physicians and 98% feel they make valuable contributions. Conclusions: A more positive, balanced, and evidence-based story can and should be told about FM and family practice.
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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 | 0.099 | 0.311 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".