Beyond headcount: four dimensions of Canada’s primary care access crisis and a three-level agenda for action
Bibliographic record
Abstract
Public debate in Canada often diagnoses a simple "shortage of family physicians," yet system indicators point to a more complex access problem. In 2023, 17% of adults reported no regular primary care provider, only 26% obtained same/next-day appointments, and about 15% of emergency department visits were potentially primary-care-manageable-over half potentially manageable virtually. Meanwhile, average weekly physician work hours have declined by 6.9 h since the late 1980's and the average number of patients seen per family physician fell from 1,746 (2013) to 1,353 (2021), alongside a shift away from comprehensive community practice. Drawing on comparative evidence that stronger primary care architecture is associated with better performance and that primary health care averages ∼13% of current health spending across OECD countries, this Perspective reframes Canada's challenge across four dimensions: effective capacity (not just headcount); demand-complexity, time, and continuity; maldistribution and loss of comprehensive care; and system entry-point design. We then organize solutions in three groups: system-level (investment floors, enrollment/rostering and after-hours obligations, payment aligned to continuity and team-based comprehensiveness), organizational-level (interdisciplinary teams, task-sharing with NPs/pharmacists/PAs, operationalized continuity), and data & research (effective-FTE and continuity metrics, complexity-adjusted panel targets, rigorous evaluation of entry-point and scope reforms). Recasting the problem from headcount to capacity-and-design clarifies actionable levers for timely attachment and sustained relational continuity.
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.034 | 0.037 |
| Scholarly communication | 0.029 | 0.014 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.024 | 0.030 |
| Insufficient payload (model declined to judge) | 0.008 | 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".