Establishing National Public Health Workforce Indicators for Monitoring and Planning
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic exposed challenges among Canada's public health workforce, including staffing shortages, burnout, and training gaps. However, national-level data remain inconsistent and fragmented. To support future planning, validated, standardized indicators are needed to assess and monitor this workforce. This study aims to develop and validate national indicators to assess workforce diversity, characteristics, skills, and needs. Methods A phased mixed-methods design is being used to modify the American Public Health Workforce Interests and Needs Survey (PH WINS) for Canada. This includes: (1) focus groups with public health decision-makers for indicator development; and (2) content and response validation with expert panels and cognitive interviews. The study is currently in its first phase. A qualitative descriptive approach is guiding six virtual focus groups (English and French) with 60 public health decision-makers (e.g., Medical Officers of Health) from diverse provinces and territories. Conventional content analysis is being used to identify key themes. Results Three virtual focus groups have been conducted with 26 public health leaders. Preliminary results suggest revisions to the PH WINS for the Canadian public health context with key themes of: a) work environment outcomes (e.g., valid measures of mental health); b) updated core competencies; c) incentives and benefit availability; d) commitment to health equity; and e) workforce composition aligned with national data standards. Conclusions Public health workforce census surveys require contextual modification. Final products will support individualized data collection for decision-making. Our study offers a framework for jurisdictions seeking to develop or adapt public health workforce indicators. Key messages • Validated, standardized indicators can guide evidence-informed strategies for recruitment, retention, workforce planning, and professional development in Canadian public health systems. • Tailored workforce indicators help capture Canada’s distinct public health needs, ensuring data reflects local contexts, informs policy, and supports equity and system-level improvement.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".