A scoping review of training and deployment policies for human resources for health for maternal, newborn, and child health in rural Africa
Bibliographic record
Abstract
BACKGROUND: Most African countries are facing a human resources for health (HRH) crisis, lacking the required workforce to deliver basic health care, including care for mothers and children. This is especially acute in rural areas and has limited countries' abilities to meet maternal, newborn, and child health (MNCH) targets outlined by Millennium Development Goals 4 and 5. To address the HRH challenges, evidence-based deployment and training policies are required. However, the resources available to country-level policy makers to create such policies are limited. To inform future HRH planning, a scoping review was conducted to identify the type, extent, and quality of evidence that exists on HRH policies for rural MNCH in Africa. METHODS: Fourteen electronic health and health education databases were searched for peer-reviewed papers specific to training and deployment policies for doctors, nurses, and midwives for rural MNCH in African countries with English, Portuguese, or French as official languages. Non-peer reviewed literature and policy documents were also identified through systematic searches of selected international organizations and government websites. Documents were included based on pre-determined criteria. RESULTS: There was an overall paucity of information on training and deployment policies for HRH for MNCH in rural Africa; 37 articles met the inclusion criteria. Of these, the majority of primary research studies employed a variety of qualitative and quantitative methods. Doctors, nurses, and midwives were equally represented in the selected policy literature. Policies focusing exclusively on training or deployment were limited; most documents focused on both training and deployment or were broader with embedded implications for the management of HRH or MNCH. Relevant government websites varied in functionality and in the availability of policy documents. CONCLUSIONS: The lack of available documentation and an apparent bias towards HRH research in developed areas suggest a need for strengthened capacity for HRH policy research in Africa. This will result in enhanced potential for evidence uptake into policy. Enhanced alignment between policy-makers' information needs and the independent research agenda could further assist knowledge development and uptake. The results of this scoping review informed an in-depth analysis of relevant policies in a sub-set of African countries.
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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 | 0.029 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.026 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".