Integrated Primary Care Workforce Planning: An Iterative Stepped Model Applied in Toronto, Canada
Bibliographic record
Abstract
Context Workforce planning supports the quintuple aim. Leading practices in planning for comprehensive primary care provision require an inclusive and data-driven approach. Objective To create an evidence-informed set of tools to support decision-making and equitable distribution of primary care human resources. Study Design & Analysis Using an integrated knowledge exchange approach, we developed and operationalized a comprehensive regional-level primary care workforce planning process, toolkit, and dashboard. Research ethics approval was deemed unnecessary. Setting The setting encompasses primary health care system in Toronto, Canada. Population Studied A range of stakeholders (including clinicians across disciplines, data stewards, analysts, local policy and health service decision-makers, and government) were engaged to identify, collect and display relevant health administrative data about population characteristics, health needs, workforce capacity and availability. Intervention/Instrument A toolkit to synthesize information needed to understand the neighbourhood-level primary care and health workforce landscapes and a dashboard to facilitate engagement with the planning process provide integrated support for evidence-informed decision-making. Results The planning process unfolds in four steps: (1) horizon scanning for relevant trends, (2) scenario generation of most impactful trends, (3) population utilization and workforce capacity modeling, and (4) policy analysis to address gaps in alignment. The toolkit builds a body of evidence around the current (and projected future) states of population health needs and primary care service provision at a neighbourhood level within the City of Toronto. The interactive dashboard is the interface between stakeholders and the planning toolkit and synthesizes the best available data to support evidence-informed planning and decision-making. Conclusion Our approach leverages international leading practices in workforce planning and knowledge exchange to make information accessible to a range of service and policy decision-makers.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".