Retention strategies and interventions for health workers in rural and remote areas: a systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of the current review is to examine the association between exposure to strategies or interventions to retain health workers in rural and remote areas of high-income countries and improved retention rates. INTRODUCTION: Attracting and retaining sufficient healthcare staff to provide adequate services for residents of rural and remote areas is an international problem. High-income countries have specific challenges in staffing remote and rural areas; despite the majority of the population clustering in large cities, a significant number of communities are in rural, remote or frontier areas which may be perceived as less attractive locations in which to live and work. INCLUSION CRITERIA: The review will consider studies that include health workers in high-income countries where participants have been exposed to interventions, support measures or incentive programs to increase retention or workforce length of employment or reduce turnover for health workers in rural and remote areas. Analytical observational studies, case-control studies, analytical cross-sectional studies, descriptive observational study designs, and descriptive cross-sectional studies published from 2010 will be eligible for inclusion. METHODS: We will use the JBI methodology for reviews of risk and etiology. A range of databases will be searched. Two reviewers will screen, critically appraise eligible articles, and extract data from included studies. Data synthesis will be conducted, where feasible, with RevMan 5.3.5. A random effects model will be used to conduct meta-analyses. We will assess the certainty of the findings using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach.
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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 | 0.090 | 0.084 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.017 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.008 |
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".