Bibliographic record
Abstract
The nursing workforce is aging rapidly, with more than 50% of the nurses eligible to retire in the next decade (Canadian Institute of Health Information, 2013). Given the aging population, Canadian nurses may not be able to support increased healthcare utilization by this older population. Since a majority of the regulated nurses in Canada are unionized, some of the strategies recommended to cope with a potential nursing shortage in the literature may not apply to unionized Canadian nurses, making collective agreements a potential source to design practices that can be used to mitigate the impact of aging on nurses' ability to work as they approach retirement age. Nine major collective agreements for registered nurses in each province governing the nursing employment relationship were analyzed to see if different practices were already addressed in collective agreements. If collective agreements are silent in any of the strategies identified in the literature, it means that healthcare organizations can adopt these practices without violating collective agreements, and may represent an opportunity for management. Five such practices were identified including providing more mentorship opportunities, encouraging nurses that are able to retire to remain in the nursing workforce, attracting internationally trained registered nurses, operational changes which may include process improvements or new technologies, as well as empowering nurses through flexibility in work schedules.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".