Regional Health Workforce Planning for Integrated Care Models: Application of a Workforce Planning Toolkit in One Ontario Health Team
Bibliographic record
Abstract
Background and Objectives: Canada is experiencing a health workforce crisis. Integrated models of care, including Ontario Health Team (OHTs), are important examples of large-scale reform focusing on local populations and a regional health workforce. To ensure a workforce meets population needs, we must use a regional approach to workforce planning. While significant work has been published on health workforce planning, few examples of how regional integrated care systems can apply workforce planning models to address population needs exist. This project aims to inform policy to support regional workforce planning for integrated care strategies. Methods: Using an exploratory mixed methods single case study design, an OHT including rural and urban populations served as the case. The selected Health Workforce Planning Model (HWPM) identified in a scoping review was applied. A mixed methods approach included multiple regional, provincial and federal data sources to describe the population, health needs and service providers. Document analysis of publicly available workforce planning documents across all OHTs was completed. Discrete analysis was conducted for quantitative data. Results: Using the HWPM, data was captured regarding service requirements (population data demographics, health status, health services utilization) and capacity (health workforce) to identify potential gaps. 6 federal/provincial data sets were used to describe the population. 19/93 regional organizations completed surveys to obtain workforce data. Review of 54 OHT websites found only 12 had documents mentioning health workforce planning, including strategic plans (66.7%) and other reports. Conclusions: Key recommendations focused on the need for: health workforce planning governance structures and accountability provincially and within OHTs; standardized and comprehensive data and reporting across sectors; and infrastructure at the provincial level and within regions to support health workforce 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 | 0.048 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| 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".