Nurses who work in rural and remote communities in Canada: a national survey
Bibliographic record
Abstract
BACKGROUND: In Canada, as in other parts of the world, there is geographic maldistribution of the nursing workforce, and insufficient attention is paid to the strengths and needs of those providing care in rural and remote settings. In order to inform workforce planning, a national study, Nursing Practice in Rural and Remote Canada II, was conducted with the rural and remote regulated nursing workforce (registered nurses, nurse practitioners, licensed or registered practical nurses, and registered psychiatric nurses) with the intent of informing policy and planning about improving nursing services and access to care. In this article, the study methods are described along with an examination of the characteristics of the rural and remote nursing workforce with a focus on important variations among nurse types and regions. METHODS: A cross-sectional survey used a mailed questionnaire with persistent follow-up to achieve a stratified systematic sample of 3822 regulated nurses from all provinces and territories, living outside of the commuting zones of large urban centers and in the north of Canada. RESULTS: Rural workforce characteristics reported here suggest the persistence of key characteristics noted in a previous Canada-wide survey of rural registered nurses (2001-2002), namely the aging of the rural nursing workforce, the growth in baccalaureate education for registered nurses, and increasing casualization. Two thirds of the nurses grew up in a community of under 10 000 people. While nurses' levels of satisfaction with their nursing practice and community are generally high, significant variations were noted by nurse type. Nurses reported coming to rural communities to work for reasons of location, interest in the practice setting, and income, and staying for similar reasons. Important variations were noted by nurse type and region. CONCLUSIONS: The proportion of the rural nursing workforce in Canada is continuing to decline in relation to the proportion of the Canadian population in rural and remote settings. Survey results about the characteristics and practice of the various types of nurses can support workforce planning to improve nursing services and access to care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".