Association between family medicine residents’ practice intentions and their initial professional activities
Bibliographic record
Abstract
Context: Amidst the ongoing crisis in family medicine (FM) in Canada, there is an increased need to gauge the future practice patterns of family physicians (FPs). Examining the intentions of FM learners provides insight into the future practice behaviors of FPs. Objective: This study examines the trends in FM residents’ intentions regarding different FM care domains compared with the actual practice patterns observed among early career FPs post-residency training Study design and analysis: Weighted, self-reported data from exiting FM residents and practicing FPs were analyzed over five years. The same cohorts at exit from training and three years into practice were examined and trends were analyzed across the years for the care domains Datasets: Data from the FM Longitudinal exit and in-practice surveys was used. Exiting cohorts of FM residents from 2016 to 2020 (average cohort n=862, average response rate=60%) and corresponding early career FPs at three years post-residency, from 2019 to 2023 (average n=318, average response rate=20%). Population Studied: Canadian FM residents and FPs at three years post-residency Instrument: The FM Longitudinal survey which is administered by Canadian FM residency programs and the College of Family Physicians of Canada Outcome measures: Practice trends in 15 domains of care Results: Analysis reveals a high correlation between reported practice intentions by exiting FM residents and the choices made in determining their future practice scope across all domains of care (r2 = 0.93). A declining trend in practice intentions among exiting FM residents was observed for over half the domains of care. In contrast, upward practice trends were observed for 9 out of 15 domains among early career FPs. Conclusion: The correlation between practice intentions when exiting residency training and decisions on actual future practice scope in early career is consistent with earlier published findings. These findings offer insight into how residency training influences the practice choices of early career FPs. The FM Longitudinal Survey data can serve as a valuable source for forecasting the future scope of family practice and help enhance health workforce planning.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".