Building Capacity for Health Workforce Planning
Bibliographic record
Abstract
Background: Health workforce planning holds promise for enhancing access to integrated care, supporting proactive and evidence-based workforce decision-making, promoting workforce sustainability, and ensuring equitable distribution of resources. Approach: Ontario Health Toronto and the Canadian Health Workforce Network co-developed and operationalized an integrated primary care workforce planning process in Toronto. The process has four iterative steps that include horizon scanning, scenario generation, workforce modeling, and policy analysis. Engagement with partners is embedded throughout the process. We aimed to spread and scale leading practices in workforce planning and to build capacity for planning by making the approach and resources available to local health system leaders. Using an action-oriented research approach, we recruited five Toronto-area Ontario Health Teams (OHTs) (93 neighbourhoods, .5 million residents) who were interested in receiving dedicated support for integrated primary care workforce planning. Over the course of 200 hours, we worked with 53 health system leaders (with backgrounds in Medicine, Nursing, Physiotherapy and Management) to build capacity for interprofessional planning through education about planning principles, and examination of local population, workforce and system trends. We integrated developmental evaluation - which monitors developments, progress, or advancements during the design or implementation of an innovation, analyzes developments for key learnings, and guides adaptations to improve the innovation - into the process. We provided OHTs with data about local population characteristics and health system use, and tools to support workforce decision-making, and used their feedback to improve planning resources. Results: Engagement and support for interprofessional planning was warmly received by health system leaders who want to make more evidence-informed decisions. Local partners recognized the value of planning and were eager for information to support decision-making but had limited knowledge and confidence. One health system leader expressed appreciation, saying; There's no way we could do this ourselves. Customized data packages validated some community and workforce trends while raising questions about others, and generated requests for additional data. Engagement activities created new connections and potential collaborations and we witnessed spontaneous sharing of ideas and solutions in real-time. A Network of Planners;, which is intended to be a Community of Practice to provide ongoing support for planning, emerged from the engagement process. Implications: A culture of planning is a necessary foundation for addressing health system challenges. Adopting leading practices in interprofessional workforce planning can help providers and system leaders develop a profile of the patients they are serving, help to estimate the services and resources needed and potential service or capacity gaps, identify future emerging issues, facilitate the use of data to monitor progress of implemented strategies and continuous quality improvement, and equip communities to advocate for the resources needed to integrate and optimize patient care. Building capacity for planning through implementing leading practices, including engagement with local partners, can enhance the impact of planning within communities and across the health system, ultimately improving health and wellbeing for all.
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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 | 0.028 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 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".