Interventions for supporting nurse retention in rural and remote areas: an umbrella review
Bibliographic record
Abstract
CONTEXT: Retention of nursing staff is a growing concern in many countries, especially in rural, remote or isolated regions, where it has major consequences on the accessibility of health services. PURPOSE: This umbrella review aims to synthesize the current evidence on the effectiveness of interventions to promote nurse retention in rural or remote areas, and to present a taxonomy of potential strategies to improve nurse retention in those regions. METHODS: We conducted an overview of systematic reviews, including the following steps: exploring scientific literature through predetermined criteria and extracting relevant information by two independents reviewers. We used the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) criteria in order to assess the quality of the reports. FINDINGS: Of 517 screened publications, we included five reviews. Two reviews showed that financial-incentive programs have substantial evidence to improve the distribution of human resources for health. The other three reviews highlighted supportive relationships in nursing, information and communication technologies support and rural health career pathways as factors influencing nurse retention in rural and remote areas. Overall, the quality of the reviews was acceptable. CONCLUSIONS: This overview provides a guide to orient future rural and remote nurse retention interventions. We distinguish four broad types of interventions: education and continuous professional development interventions, regulatory interventions, financial incentives, and personal and professional support. More knowledge is needed regarding the effectiveness of specific strategies to address the factors known to contribute to nurse retention in rural and remote areas. In order to ensure knowledge translation, retention strategies should be rigorously evaluated using appropriate designs.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".