Changing primary care capacity in Canada: protocol for a cross-provincial mixed methods study
Bibliographic record
Abstract
INTRODUCTION: Despite having more family physicians (FPs) and nurse practitioners (NPs) per capita than ever before in Canada, there is a clear gap between population primary care needs and system capacity. Primary care needs may be shaped by population ageing, increasing clinical and social complexity and growing service intensity. System capacity may be shaped by falling practice volumes, increasing administrative workload, changing clinician demographics and new health system roles (eg, hospitalist and focused practices). These changing factors could contribute to reduced patient access to primary care, worsened health inequities and stress and overwork among primary care clinicians. Workforce planning tools used in most countries do not adequately consider these factors. Our study will identify and explore factors shaping population service use and system capacity over time and develop planning tools to estimate future primary care needs and capacity. METHODS AND ANALYSIS: We will interview FPs and NPs about factors shaping workload, including patient characteristics, practice expectations and system context. This will inform analysis of administrative data to describe factors shaping primary care need (patient demographics, clinical and social complexity, service intensity) and capacity (provider supply, demographics, service volume, roles) over a 20-year period from 2004/2005 to 2023/2024. Qualitative and quantitative findings will inform analytical models that project and compare need and capacity under stakeholder-informed scenarios. The study includes the Canadian provinces of British Columbia, Manitoba, New Brunswick and Nova Scotia, provinces with varied policy and population contexts and complementary administrative health data. ETHICS AND DISSEMINATION: Research ethics board (REB) approval for the qualitative study has been provided by Research Ethics BC, with subsequent approvals from Horizon Health Network, Nova Scotia Health, University of Manitoba and University of Ottawa. REB approval for analysis of linked administrative data was obtained from the Nova Scotia Health REB, Research Ethics BC, University of Manitoba and University of New Brunswick. Our findings will support primary care capacity planning to equitably meet the needs of a growing and ageing population.
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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 | 0.077 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.066 | 0.008 |
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".