Primary care workforce planning within integrated care models: Application of a workforce planning toolkit
Bibliographic record
Abstract
Context: There is a crisis in the primary care workforce in Canada. While approaches have been developed to support primary care health human resource (HHR) planning, most planning is conducted within sector silos and there are few examples that consider a regional approach within an integrated care system. Objectives: 1.Apply a HHR planning framework to one Ontario Health Team, a model of integrated care. 2. Describe the primary care workforce from a regional lens. 3. Develop policy to support regional workforce planning. Study Design and Analysis: An exploratory single case study design with concurrent mixed-methods, including key informant interviews, publicly available data and surveys. Regional human resources and population health data was collected from multiple data sources including federal, provincial/territorial, regional, and hospital/primary care levels, along with surveys to all providers. Descriptive analysis was conducted for quantitative data. Qualitative data was analyzed used thematic analysis. Data were merged through pattern matching. Policy recommendations were finalized using a deliberative dialogue. Setting/Data sets: One OHT in Eastern Ontario, Canada. Seven federal and provincial data sets, including professional colleges. Population Studied: OHT health workforce, including primary care and its attributed patient population. Intervention: The Integrated Primary Care Workforce Planning Toolkit was applied to one OHT. Results: Fourteen interviews described contextual factors including the current status, driving forces around regional planning, capacity for change and innovative solutions. Data was highly inconsistent across organizations and wide variation in the operational definitions used. Just over half of the attributed population were rostered to a team-based primary care model. Five data sources were required to describe the primary care workforce with an estimated 163 physicians, 33 NPs and 138 interprofessional providers. No consolidated data source existed for non-physician providers and provider raw numbers do not convey details needed for planning. A set of policy recommendations focused on: 1) HHR governance structure within OHTs, 2) consolidation of the primary care workforce, and 3) data and reporting.9 Conclusions: There remain significant challenges in obtaining data to inform primary care workforce planning and little infrastructure within Ontario Heath Teams to support regional planning.
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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 | 0.031 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".