Mental health nursing practice in rural and remote Canada: Insights from a national survey
Bibliographic record
Abstract
Access to and delivery of quality mental health services remains challenging in rural and remote Canada. To improve access, services, and support providers, improved understanding is needed about nurses who identify mental health as an area of practice. The aim of this study is to explore the characteristics and context of practice of registered nurses (RNs), licensed practical nurses (LPNs), and registered psychiatric nurses (RPNs) in rural and remote Canada, who provide care to those experiencing mental health concerns. Data were from a pan-Canadian cross-sectional survey of 3822 regulated nurses in rural and remote areas. Individual and work community characteristics, practice responsibilities, and workplace factors were analysed, along with responses to open-ended questions. Few nurses identified mental health as their sole area of practice, with the majority of those being RPNs employed in mental health or crisis centres, and general or psychiatric hospitals. Nurses who indicated that mental health was only one area of their practice were predominantly employed as generalists, often working in both hospital and primary care settings. Both groups experienced moderate levels of job resources and demands. Over half of the nurses, particularly LPNs, had recently experienced and/or witnessed violence. Persons with mental health concerns in rural and remote Canada often receive care from those for whom mental health nursing is only part of their everyday practice. Practice and education supports tailored for generalist nurses are, therefore, essential, especially to support nurses in smaller communities, those at risk of violence, and those distant from advanced referral centres.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".