Sustaining the Canadian Nursing Workforce: Targeted Evidence-Based Reactive Solutions in Response to the Ongoing Crisis
Bibliographic record
Abstract
Inadequate staffing, excessive workloads, endemic violence and unhealthy workplaces are some of the challenges facing Canadian nurses. Leaving these issues unaddressed has had pernicious impacts on the nursing workforce: thousands of nurses across Canada have been suffering from extreme stress, anxiety and burnout, leading many of them to leave their current jobs and, for some, the profession of nursing altogether. We conducted a comprehensive yet rapid review of evidence-based solutions from the peer-reviewed and policy literature, stakeholder dialogues and member surveys commissioned by the Canadian Federation of Nurses Unions that could be implemented and scaled across Canada. Our findings support coordinated series of collectively planned, carefully sequenced and evidence-based interventions to retain, return, integrate and recruit nurses targeted to support the nursing workforce from training to early-, mid- and late-career stages. The implementation of these reactive solution bundles will also enhance the quality of healthcare services and, more broadly, the healthcare system.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".