Co-creating Our Future: Health Human Resource Planning in Canada, Reimagined
Bibliographic record
Abstract
Background: Currently, Canada’s health care system operates in provincial/territorial silos with no coordinated health workforce planning efforts, leading to provider burnout and issues with access to care. A needs-based integrated health human resources planning framework will help ensure the population has access to care when and where they need it and ensure equitable, compassionate and sustainable conditions for the health workforce. As part of Impact 2040, the Canadian Medical Association’s (CMA’s) bold strategic plan, health workforce planning was identified as a key priority. Previous attempts at a national physician workforce plan have been unsuccessful, hence a different approach is needed. To have a truly integrated health human resources plan (IHHRP), shared commitment and collective desire to move toward implementation, we need to work collaboratively with those who provide care, access care and organize health care delivery in Canada. What we did: CMA's integrated health human resources team adopted a design thinking methodology to advance efforts on IHHR planning in Canada. Design thinking brings together diverse skills to look at old problems in new ways, while providing structure and guidance in the form of short, timebound activities. Through this process, we arrived at the concept of collaborative, integrated health workforce planning with interprofessional groups and patient representatives. The idea of co-creating health workforce planning was welcomed by testers, and feedback on the type of process and event was received. Over two days in October 2023, the Canadian Medical Association (CMA) hosted the first co-creation event with representatives from a diverse group of more than 40 national and provincial healthcare organizations and the CMA’s Patient Voice group. Professions represented included, but were not limited to medicine, nursing, pharmacy, physiotherapy, occupational therapy, physician assistants, and psychology, from a wide range of specialties, and sectors, including health worker regulation and education. The aim of the meeting was to create a shared vision of integrated health human resource planning (IHHRP) in Canada for a re-imagined future healthcare system - Building on collaborative work done in other settings attendees worked through a series of break-out sessions as small groups in a facilitated process to determine: -Existing barriers to effective IHHRP -A vision and ideal state for the future of IHHRP -Foundational principles of a patient-partnered framework -Implementation considerations, including conditions for success. -Next steps Results: Following extensive discussions and prioritization by participants, five variables were identified as priority health workforce planning components that would make up a pan-Canadian framework to reach the ideal state for integrated health workforce planning: -Patient partnered, rooted in community -Governance -Data and insights -Social determinants of health -Integrated workforce Learning: Feedback collected was positive overall. Participants specified that co-creating health workforce planning has the potential for ‘transformative change’. Participants indicated a willingness to continue to work collaboratively, co-creating not only the output, but the process, working together to identify essential next steps for framework development, moving toward implementation. Additional steps in co-creation process design, and framework development are in progress through early 2024, and additional results and learnings will be available.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.036 | 0.016 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".