The Trajectory of Agency-Employed Nurses in Ontario, Canada: A Longitudinal Analysis (2011–2021)
Bibliographic record
Abstract
In Canada, reports of nursing staff shortages, job vacancies and the use of private agency nurses, especially in hospitals, have increased since the start of the COVID-19 pandemic. Media reports suggest the pandemic exacerbated nursing shortages among other issues, and nurses are leaving their traditional positions to work at such agencies. Public spending on agency nurses has increased appreciably. Using 2011 to 2021 regulatory college data on all registered nurses (RNs) and registered practical nurses (RPNs) in the province of Ontario, Canada, we investigated trends in the count and share of nurses working for employment agencies. We also examined the rate at which previously non-agency employed nurses transition to employment in at least one agency job. We found the prevalence of RNs and RPNs reporting agency employment was relatively stable from 2011 to 2019, and decreased slightly in 2020 and 2021. However, there was a small increase in transitions from non-agency employment to working at an agency job. We also found the mean hours of practice in all jobs reported by agency and non-agency nurses increased during the pandemic. Based on these findings, an increase in hours and/or prices for agency nurses may explain the increase in public funding for agency nurses, but it was not driven by an increasing share of nurses working for employment agencies. To fully understand employment agency activity, policymakers may need to monitor hours of work and hourly costs rather than only costs. Further research is required to investigate any long-term effects the pandemic may have had on agency-employment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".