Growing Old Together: The Influence of Population and Workforce Aging on Supply and Use of Family Physicians
Bibliographic record
Abstract
Canadians have expressed concern that access to a family physician (FP) has declined precipitously. Yet FP-topopulation ratios remained relatively stable over the last decade, and there were perceptions of physician surpluses, at least in urban centres, 10 years ago. We evaluated whether demographic changes among patients and FPs, and in the volume of care received and provided over the period, contribute to this paradox. Given the relationship between age and FP use in fiscal year 1991/1992, an aging population should have been associated with a 2 per cent increase in visits by 2000/2001. Likewise, given the relationship between FP age and workloads in 1991/1992, an aging workforce should have been associated with a 12 per cent increase in service provision a decade later. Yet visit rates and average FP workloads remained unchanged. There was an increase in age-specific rates of FP use among older adults and a decline in rates among the young, and an increase in age-specific workloads such that older FPs provided many more services than their predecessors (30%) and younger FPs provided many fewer (20%). In terms of impact on future requirements for FPs, both changes in age-specific rates of use, and changes in age-specific patterns of FP productivity, trump population aging as key drivers.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".