Interventions for improving attraction and retention of health workers in rural and underserved areas: a systematic review of systematic reviews
Bibliographic record
Abstract
BACKGROUND: Global health workforce shortages exist with disparities in the skill mix and distribution of health workers. Rural and underserved populations are often disadvantaged in terms of access to health care. METHODS: This systematic review summarized all systematic reviews that assessed interventions for improving attraction and retention of health workers in rural and underserved areas. We systematically searched selected electronic databases up to 31 March 2020. The authors independently screened the reviews, extracted data and assessed the certainty of evidence using GRADE. Review quality was assessed using the ROBIS tool. RESULTS: There was a paucity of evidence for the effectiveness of the various interventions. Regulatory measures were able to attract health workers to rural and underserved areas, particularly when obligations were attached to incentives. However, health workers were likely to relocate from these areas once their obligations were completed. Recruiting rural students and rural placements improved attraction and retention although most studies were without control groups, which made conclusions on effectiveness difficult. CONCLUSIONS: Cost-effective utilization of limited resources and the adoption and implementation of evidence-based health workforce policies and interventions that are tailored to meet national health system contexts and needs are essential.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".