Engagement with partners is a leading practice in health workforce planning: What health leaders need to know
Bibliographic record
Abstract
Workforce planning ensures that the health workforce is aligned with current and future population needs. Engagement with partners and knowledge users is a leading practice in planning and is essential for planning to be successful. The goal of this study was to explore the considerations and processes involved in integrating engagement into workforce planning. Through a case study of primary care workforce planning in Toronto, we address the role of engagement, how it can be integrated into planning, and how lessons from engagement support spread and scale of effective workforce planning. In the course of engagement with five Ontario Health Teams between September 2023 and February 2024, we learned that there is considerable enthusiasm for planning, but that support is needed, and that engagement guides investment and strengthens relationships. We offer guidance for leaders with respect to actualizing engagement and building capacity for health workforce planning across the health system.
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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 | 0.045 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".