Do Recent Family Physician Graduates Practice Differently? A Longitudinal Study Four Canadian Provinces
Bibliographic record
Abstract
Context: It is becoming more difficult to find a family physician in Canada. This has led to speculation that new family physicians may be to blame because they are more likely to provide lower service volume and less likely to provide longitudinal primary care. Objective: To investigate the impact of graduation cohort on family physician practice volume and continuity of care over two decades. Study Design and Analysis: Retrospective-cohort of family physicians from 1997/98 to 2017/18. Median polish analysis of patient contacts and physician-level continuity was completed to isolate years in practice, period, and cohort effects. Dataset: Administrative health and physician claims data were collected in British Columbia, Manitoba, Ontario, and Nova Scotia, Canada. Databases included registry files from provincial regulatory colleges, physician billing information, and patient registration files for provincial insurers. Population Studied: All physicians registered with their respective provincial regulatory colleges with a medical specialty of family practice and/or billed the provincial health insurance system for patient care as FPs. Outcome Measures: Patient contacts (count of unique patient-physician day combinations) and physician-level continuity (proportion of total annual contacts — excluding ED visits — that all patients seen by an FP had with that FP). Results: Median patient contacts per provider fell over time in the four provinces examined. In all provinces, median contacts increased with years in practice until mid-to-late-career and declined into end-of-career. We found no relationship between graduation cohort and practice volume or FP-level continuity. Conclusions: Declines in service volume were observed in all provinces, with expected trajectories of service volume and continuity over a FP–s career. We found no generational differences in FP practice. These findings are important for health workforce planning in primary care sectors across the country, and for the general discourse concerning the behaviours and preferences of recent medical graduates. Our findings highlight that intergenerational tension and blame is unfounded and only distracts from important issues in workforce planning in primary care sectors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".