"I'm a Different Kind of Nurse": Advice from Nurses in Rural and Remote Canada
Bibliographic record
Abstract
The sustainability of the rural and remote nursing workforce in Canada is increasingly at issue as the country becomes more urbanized and the nursing workforce ages. In order to support the retention of nurses in rural and remote communities and the recruitment of nurses to these communities, we require a better understanding of what is important to rural and remote nurses themselves. As part of the in-depth interviews conducted within The Nature of Nursing Practice in Rural and Remote Canada, a national research project, registered nurses (RNs) were asked what advice they would have for new nurses, educators, administrators and policy makers. This is the first of two papers describing that advice. It focuses on RNs in acute care, long-term care, home care, community health/public health and primary care roles in rural and remote communities across the country. The RNs were generous with their advice and gave many rich examples. While they were enthusiastic about their nursing practice and encouraging of other nurses to work in rural settings, they were intent that improvements be made in several key areas: education available to new practitioners and themselves, working conditions for rural and remote nurses, leadership, organizational supports and policies that better support rural and remote practice and communities.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".