Rural and Remote Continuing Nursing Education: An Integrative Literature Review
Bibliographic record
Abstract
Background: Rural and remote nursing has unique practice requirements that create a need for distinct education and practice preparation. Preparing registered nurses (RNs) to work in rural and remote communities is essential for the support and advancement of rural and remote health, as there is a shortage of rural and remote health care providers. Purpose: An integrative literature review was conducted to identify the current continuing education needs of rural and remote RNs internationally. Sample: Eight studies were included in the integrative review of the literature. Countries reported in the literature included Canada (n = 2), Australia (n = 2), Sweden (n = 1) and the United States (n = 3). Method: An integrative literature review on rural and remote nursing practice continuing education was conducted using Torraco’s (2005) guidelines, in addition to Whittemore and Knafl’s (2005) methodological strategies. A search strategy was created, tested, and approved by the research team.Themes were extracted, collated, analyzed, and knowledge synthesized. Findings: Rural and remote RNs identified areas requiring enhanced ongoing training. The identified training areas were summarized into the following four themes: 1) Comprehensive specialized nursing practice for direct patient care, 2) Unanticipated events, 3) Non-direct patient care, and 4) Advanced specialty courses. Conclusion: The autonomy, competency, and expertise that is expected of RNs working in rural and remote locations requires educational supports. Rural and remote nursing continuing education is required in the areas of: comprehensive specialized nursing practice for direct patient care, unanticipated events, non-direct patient care, and advanced specialty courses. Keywords: continuing education, integrative review, registered nurse(s), remote, rural Acknowledgements: The authors thank Saskatchewan Polytechnic for partial funding of this review through the Seed Applied Research Program. The authors also thank their research team member Chau Ha and research assistant Devendrakumar Kanani for their contributions to this integrative review.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".