Perspectives of policymakers and health care managers on the retention of health workers in rural and remote settings in Nigeria
Bibliographic record
Abstract
BACKGROUND: Health workers are indispensable to service delivery especially in rural and remote communities where the burden of disease is high. Nigeria faces numerous human resources for health challenges, health workers are reluctant to take up rural postings, and the government is struggling to implement planned interventions due to staff shortages. This study explored the perspectives of policymakers and primary health care (PHC) managers on factors that hinder health workers from staying in rural and remote areas and strategies for improving retention. METHODS: We interviewed purposively selected 10 policymakers and 20 PHC managers in Bauchi and Cross River States, Nigeria. RESULTS: Respondents identified a lack of basic social amenities, the poor state of infrastructure, poor working conditions, remuneration and the barrier to career advancement as factors that impede health workers from taking up rural postings. Strategies for improving retention include enforcing bonding; paying salaries promptly, increase in rural allowances and prioritizing health workers in rural and remote areas for capacity building. CONCLUSION: The results of the study indicate the importance of applying context-specific strategies aimed at ensuring the availability of social amenities such as roads, water, electricity, telecommunication, security, the status of infrastructure, working conditions and remunerations.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".