Implications of absenteeism of health workers on achieving universal health coverage in Nigeria: exploring lived experiences in primary healthcare
Bibliographic record
Abstract
Primary healthcare facilities are the bedrock for achieving universal health coverage (UHC) because of their closeness to the grassroots and provision of healthcare at low cost. Unfortunately, in Nigeria, the access and quality of health services in public primary healthcare centres (PHCs) are suboptimal, linked with persistent occurrence of absenteeism of health workers. We used a UHC framework developed by the World Health Organization-African Region to examine the link between absenteeism and the possible achievement of UHC in Nigeria. We undertook a qualitative study to elicit lived experiences of healthcare providers, service users, chairpersons of committees of the health facilities, and policymakers across six PHCs from six local government areas in Enugu, southeast Nigeria. One hundred and fifty participants sourced from the four groups were either interviewed or participated in group discussions. The World Health Organization-African Region UHC framework and phenomenological approach were used to frame data analysis. Absenteeism was very prevalent in the PHCs, where it constrained the possible contribution of PHCs to the achievement of UHC. The four indicators toward achievement of UHC, which are demand, access, quality, and resilience of health services, were all grossly affected by absenteeism. Absenteeism also weakened public trust in PHCs, resulting in an increase in patronage of both informal and private health providers, with negative effects on quality and cost of care. It is important that great attention is paid to both availability and productivity of human resources for health at the PHC level. These factors would help in reversing the dangers of absenteeism in primary healthcare and strengthening Nigeria's aspirations of achieving UHC.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".