Trends and indicators of nursing workforce shortages in Canada: a retrospective ecological study, 2015–2022
Bibliographic record
Abstract
OBJECTIVES: To examine longitudinal trends and identify key indicators of nursing workforce shortages across Canadian provinces and territories using publicly available data. DESIGN: Retrospective ecological study. SETTING: Primary and secondary care in Canada. National data were extracted from Statistics Canada and the Canadian Institute for Health Information (CIHI) between 2015 and 2022 at the provincial and territorial levels. PARTICIPANTS: The study included registered nurses and registered psychiatric nurses employed in the Canadian healthcare system. Licensed practical nurses and nurse practitioners were excluded. Territories with missing data were excluded from the analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was nursing workforce shortage, assessed in relation to potential indicators, including the nurse-to-population ratio, job vacancy rate and annual costs of overtime work, using structural equation modelling (SEM). RESULTS: The Canadian nursing workforce grew by 8.0%, with the nurse-to-population ratio increasing from 11.08 to 12.13 per 1000 population. Job vacancies rose by 6.4% (95% CI: 6.29 to 6.51%), overtime hours increased by 13.09 million (95% CI: 10.28 to 15.87) and yearly overtime costs rose by 0.78 billion CAD (95% CI: 0.64 to 0.92). SEM revealed significant associations between workforce shortage and the nurse-to-population ratio (standardised β=0.863, 95% CI: 0.942 to 0.975), job vacancy rate (β=0.958, 95% CI: 0.927 to 0.990) and yearly overtime costs (β=0.983, 95% CI: 0.967 to 0.999). Predicted shortage scores were lower before 2020 but increased significantly after 2020, potentially reflecting the impact of COVID-19 pandemic. CONCLUSIONS: Despite growth in the nursing workforce, increasing job vacancies, overtime hours and costs highlight persistent shortages. Monitoring these indicators is essential for effective workforce planning and sustainable healthcare delivery.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".