New Strategies for Monitoring the Health of Canadian Nurses: Results of Collaborations with Key Stakeholders
Bibliographic record
Abstract
The aim of this descriptive study was to help policy- and decision-makers enhance the health of the Canadian nursing workforce by highlighting key factors of concern and exploring options for collecting and utilizing nurses' health data. This paper describes the views of 62 nursing stakeholders from a diverse spectrum of professional, labour, management and government perspectives from across Canada, regarding key factors contributing to work-related health problems in the nursing profession, particularly those relating to the work environment and hospital restructuring. The results were combined with a synthesis of existing information sources about the health of nurses in Canada. With respect to the key concerns, musculoskeletal conditions/injuries and stress and burnout were identified as nurses' major work-related health problems. An examination of the data synthesis inventory revealed that no existing data sources can adequately profile nurses' health, especially in relation to the components of the Conceptual Model of Nurses' Health developed in the study. Three strategies for monitoring nurses' health are proposed.
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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 | 0.029 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".