A Checklist for Implementing Rural Pathways to Train, Develop and Support Health Workers in Low and Middle-Income Countries
Bibliographic record
Abstract
Background: There is an urgent need to scale up global action on rural workforce development. This World Health Organization-sponsored research aimed to develop a Rural Pathways Checklist. Its purpose was to guide the practical implementation of rural workforce training, development, and support strategies in low and middle-income countries (LMICs). It was intended for any LMICs, stakeholder, health worker, context, or health problem. Method: Multi-methods involved: (1) focus group concept testing; (2) a policy analysis; (3) a scoping review of LMIC literature; (4) consultation with a global Expert Reference Group and; (5) field-testing over an 18-month period. Results: The Checklist included eight actions for implementing rural pathways in LMICs: establishing community needs; policies and partners; exploring existing workers and scope; selecting health workers; education and training; working conditions for recruitment and retention; accreditation and recognition of workers; professional support/up-skilling and; monitoring and evaluation. For each action, a summary of LMICs-specific evidence and prompts was developed to stimulate reflection and learning. To support implementation, rural pathways exemplars from different WHO regions were also compiled. Field-testing showed the Checklist is fit for purpose to guide holistic planning and benchmarking of rural pathways, irrespective of LMICs, stakeholder, or health worker type. Conclusion: The Rural Pathways Checklist provides an agreed global conceptual framework for the practical implementation of “grow your own” strategies in LMICs. It can be applied to scale-up activity for rural workforce training and development in LMICs, where health workers are most limited and health needs are greatest.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".