Educational Needs of Rural Nurses when Entering Practice
Bibliographic record
Abstract
Purpose: The purpose of this integrative review is to identify the educational needs of rural nurses and the strategies that have been effective in meeting those education needs. Sample: The literature search yielded 1388 articles to be screened after 930 duplicates were removed. Two researchers screened by title and abstract and full-text review, yielding ten articles. Four were qualitative, four were quantitative and two studies were mixed methods. Method: An integrative review using Whittemore and Knafl’s method was conducted. CINAHL, MEDLINE, EMBASE and Scopus databases were searched. Studies about registered nurses' practice from Canada, the United States and Australia were included as these countries are geographically large with rural areas at a distance from larger, urban centres. Inductive content analysis was used to develop themes. Findings: The themes of educational needs, educational delivery, and barriers to education were developed from data analysis. Educational needs of rural nurses are well established, although multiple barriers impede access to education. Various educational delivery methods have been attempted; it is unclear as to which method is most effective. Conclusions: Rural nurses must continue to advocate for education opportunities specific to their needs and the demands related to working within the rural context. It’s essential employers and accrediting bodies of hospitals work together to ensure that rural and remote nurses have the essential skills to care for rural and remote populations. Keywords: rural nursing, educational needs, educational delivery, barriers to education.DOI: https://doi.org/10.14574/ojrnhc.v23i1.723
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".