How decentralisation influences the retention of primary health care workers in rural Nigeria
Bibliographic record
Abstract
BACKGROUND: In Nigeria, the shortage of health workers is worst at the primary health care (PHC) level, especially in rural communities. And the responsibility for PHC - usually the only form of formal health service available in rural communities - is shared among the three tiers of government (federal, state, and local governments). In addition, the responsibility for community engagement in PHC is delegated to community health committees. OBJECTIVE: This study examines how the decentralisation of health system governance influences retention of health workers in rural communities in Nigeria from the perspective of health managers, health workers, and people living in rural communities. DESIGN: The study adopted a qualitative approach, and data were collected using semi-structured in-depth interviews and focus group discussions. The multi-stakeholder data were analysed for themes related to health system decentralisation. RESULTS: The results showed that decentralisation influences the retention of rural health workers in two ways: 1) The salary of PHC workers is often delayed and irregular as a result of delays in transfer of funds from the national to sub-national governments and because one tier of government can blame failure on another tier of government. Further, the primary responsibility for PHC is often left to the weakest tier of government (local governments). And the result is that rural PHC workers are attracted to working at levels of care where salaries are higher and more regular - in secondary care (run by state governments) and tertiary care (run by the federal government), which are also usually in urban areas. 2) Through community health committees, rural communities influence the retention of health workers by working to increase the uptake of PHC services. Community efforts to retain health workers also include providing social, financial, and accommodation support to health workers. To encourage health workers to stay, communities also take the initiative to co-finance and co-manage PHC services in order to ensure that PHC facilities are functional. CONCLUSIONS: In Nigeria and other low- and middle-income countries with decentralised health systems, intervention to increase the retention of health workers in rural communities should seek to reform and strengthen governance mechanisms, using both top-down and bottom-up strategies to improve the remuneration and support for health workers in rural communities.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".