Factors influencing recruitment and retention of healthcare workers in rural and remote areas in developed and developing countries: an overview
Bibliographic record
Abstract
Shortage of healthcare workers in rural and remote areas remains a growing concern both in developed and developing countries. This review aims to synthesize the significant factors impacting healthcare professionals' recruitment and retention in rural and remote areas, and to identify those relevant for developing countries. This paper included the following steps: exploring scientific literature through predetermined criteria and extracting relevant information by two independents reviewers. The AMSTAR tool was used to assess the methodological quality. Of the 224 screened publications, 15 reviews were included. Four reviews focused on recruitment factors, and another four reviews focused on retention factors. The remaining focused both on recruitment and retention factors. The most important factors influencing recruitment were rural background and rural origin, followed by career development. Opportunities for professional advancement, professional support networks and financial incentives were factors impacting retention. While the main factors influencing recruitment and retention have been largely explored in the literature, the evidence on strategies to reduce the shortage of healthcare workers in rural area, particularly in developing countries, is low. Further research in this field is needed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".