The Crisis in the Nursing Labour Market: Canadian Policy Perspectives
Bibliographic record
Abstract
The labour market for care professionals has experienced significant changes, resulting in critical shortages globally. Nurses represent the largest share of health workers worldwide; nonetheless, an estimated 13 million more nurses will be needed over the next 10 years. Prior to the pandemic, the domestic supply of nurses in Canada had not kept pace with the ever-increasing demand for services. Pre-pandemic age- and needs-based forecasting models have estimated shortages in an excess of 100,000 nurses nationwide by 2030. While COVID-19 has accelerated the demand for and complexity of service requirements, it has also resulted in losses of healthcare professionals due to an increased sick leave, unprecedented burnout and retirements. This paper examines key factors that have contributed to nursing supply issues in Canada over time and provides examples of policy responses to the present shortage facing the healthcare system. To provide adequate care, the nursing workforce must be stabilized and-more importantly-recognized as critical to the health of the population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 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".