How is ‘shortage’ defined? Exploring Nursing Workforce Data across Canada 2015-2022: An Ecological Study
Bibliographic record
Abstract
RationaleDetermining the ability of a country’s nursing workforce to meet the health care needs of the population is essential for optimal health outcomes. ‘Nursing shortage’ is frequently heralded as an issue, yet it is unclear how ‘shortage’ is defined and calculated. The purpose of this study was to collect and link publicly available Canadian data to describe and compare trends in nursing workforce capacity. MethodsPrimary data sources included linking Statistics Canada and Canadian Institutes of Health Information (CIHI) data from 2015 to 2022. Statistics Canada tracks provincial population data and job vacancy rates. CIHI receives data from provincial nursing organizations on demographics, roles, and employment status. To estimate a sufficient workforce, job vacancy rates (a proxy for provincial need) were cross tabulated with the number of registered nurses (RNs) and registered psychiatric nurses (RPNs) per year. ResultsThe number of RNs and RPNs in Canada has increased by 8.6% between 2015 and 2022, to a total of 322,226. Job vacancies, as a percent of total nursing supply, shows a rising trend (2.3% to 8.7%) between 2015 and 2022 across Canada. In 2022, 84.9% of Canadian RNs and RPNs in direct patient care across Canada and 86.1% were in urban settings. Conclusion & LimitationsThis project examines Canadian nursing workforce data encompassing potential effects of the COVID-19 pandemic. The trends are limited to annual due to aggregated data. Data on a country’s nursing workforce measured monthly and consistently across provinces would yield clearer information.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 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".