Characteristics of patients attached to near-retirement family physicians: a population-based serial cross-sectional study in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: Population ageing is a global phenomenon. Resultant healthcare workforce shortages are anticipated. To ensure access to comprehensive primary care, which correlates with improved health outcomes, equity and costs, data to inform workforce planning are urgently needed. We examined the medical and social characteristics of patients attached to near-retirement comprehensive primary care physicians over time and explored the early-career and mid-career workforce's capacity to absorb these patients. DESIGN: A serial cross-sectional population-based analysis using health administrative data. SETTING: Ontario, Canada, where most comprehensive primary care is delivered by family physicians (FPs) under universal insurance. PARTICIPANTS: All insured Ontario residents at three time points: 2008 (12 936 360), 2013 (13 447 365) and 2019 (14 388 566) and all Ontario physicians who billed primary care services (2008: 11 566; 2013: 12 693; 2019: 15 054). OUTCOME MEASURES: The number, proportion and health and social characteristics of patients attached to near-retirement age comprehensive FPs over time; the number, proportion and characteristics of near-retirement age comprehensive FPs over time. SECONDARY OUTCOME MEASURES: The characteristics of patients and their early-career and mid-career comprehensive FPs. RESULTS: Patient attachment to comprehensive FPs increased over time. The overall FP workforce grew, but the proportion practicing comprehensiveness declined (2008: 77.2%, 2019: 70.7%). Over time, an increasing proportion of the comprehensive FP workforce was near retirement age. Correspondingly, an increasing proportion of patients were attached to near-retirement physicians. By 2019, 13.9% of comprehensive FPs were 65 years or older, corresponding to 1 695 126 (14.8%) patients. Mean patient age increased, and all physicians served markedly increasing numbers of medically and socially complex patients. CONCLUSIONS: The primary care sector faces capacity challenges as both patients and physicians age and fewer physicians practice comprehensiveness. Nearly 15% (1.7 million) of Ontarians may lose their comprehensive FP to retirement between 2019 and 2025. To serve a growing, increasingly complex population, innovative solutions are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".