Leveraging National Labour and Health Data for Strategic Health Workforce Planning: Insights From Canadian Case Studies Using Statistics Canada Data Sources
Bibliographic record
Abstract
This article showcases the high-quality, standardized, and national labour force and health-related data that can be leveraged for effective health workforce planning. It also underscores the importance of interoperability, the ability to integrate and harmonize data from multiple sources to optimize health workforce analysis. Using three case studies drawing on five Statistics Canada data sources, it examines persistent shortages of nurses and personal support workers and the impact of increased workload on their stress during the COVID-19 pandemic. This article also outlines how Statistics Canada data can inform planning by identifying unmet labour demand, work-related stress, and untapped labour resources, such as internationally educated healthcare professionals. It aims to guide health leaders in accessing and leveraging Statistics Canada data, including but not limited to those outlined here, to strategically address workforce and policy challenges in the health sector using an evidence-based approach.
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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 | 0.034 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.032 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".