Interventions for improving attraction and retention of health workers in rural and underserved areas: a systematic review of systematic reviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.048 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 it