Developing a profile of first year family medicine (FM) residents in Canada: Perceptions and practice intentions 2017-2022
Bibliographic record
Abstract
Context: With access to comprehensive primary care posing challenges in Canada and family medicine education reform on the horizon, understanding characteristics of those choosing family medicine as a specialty is increasingly important. Establishing baseline criteria to support selection processes could lead to a complement of family physicians who are more willing to practice comprehensive family medicine in any community in Canada. Objective: To provide a profile of first year family medicine (FM) resident learning experiences, practice intentions and perceptions of FM as a specialty in Canada using data from the College of Family Physicians of Canada’s (CFPC) Family Medicine Longitudinal Survey (FMLS). Study Design and Analysis: Data from 6,054 FM first year residents across all 17 FM residency programs in Canada who completed the FMLS from 2017 to 2022 were analyzed (average RR=66%). The FMLS is a self-reported survey following residents from entry into residency, at exit, and three years post residency. Ethics approvals/exemptions were obtained from all participating universities. Descriptive and weighted bivariate and trends analysis were performed to evaluate first year FM residents’ demographics, perceptions, and intentions to practice comprehensively upon graduation. Datasets: The CFPC’s FM Longitudinal Survey data collected from 2017-2022 Population Studied: First year FM residents across all 17 residency programs in Canada. Intervention/Instrument: FM Longitudinal Survey (FMLS) Outcome Measures: FM residents’ demographics, perceptions of FM specialty, and future practice intentions including intent to practice comprehensively. Results: Across all six years, at entry to residency, 90% of respondents on average were proud to become family physicians. An increasing percent of residents agree that medical specialists have little respect for family physicians (40% in 2017 vs 48% in 2022) and an increasing percent would prefer to be in a specialty other than FM (7% in 2017 vs 10% in 2022). A decreasing percent of residents agree that government perceives FM as essential to the health care system and residents also reported less intention to practice comprehensive care. Conclusions: The findings highlight the need to explore reasons for this change and how to reverse the trend particularly during a time when fewer medical students are choosing family medicine and those who are less likely to practice the type of comprehensive care needed in Canada.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".