Critical review of healthcare financing and a survey of system quality perception among healthcare users in Nigeria (2010–2023)
Bibliographic record
Abstract
Nigeria aims to enhance its healthcare quality index score of 84th out of 110 countries and its Sustainable Development Goals Index ranking of 146th out of 166. Due to increased population, disease burden, and patient awareness, healthcare demand is rising, putting pressure on funding and quality assurance. The Nigerian healthcare financing and its impacts are complex; this study gives insights into the trends. This questionnaire-based cross-sectional survey (conducted from June to August 2023) and 2010-2023 health budget analysis examined healthcare finance patterns and user attitudes (utilisation, preference and quality perceptions) in Nigeria. Data from government health budgets and a stratified random sample of 2,212 from nine states, obtained from the socioculturally diverse 237 million population, were analysed with a focus on trends, proportions, frequency distributions, and tests of association. Results show that the average rating of healthcare experiences did not vary significantly over the last decade. Healthcare system quality was rated mainly poor or very poor; structure (74.09%), services (61.66%), and cost (60.89%). While 87.36% used government healthcare facilities, 85.00% paid out-of-pocket, and 72.60% of them were dissatisfied with the value for money. Despite a preference for government facilities (71.43%), respondents cited high costs (62.75%), poor funding (85.65%), inadequate staffing (90.73%), and lack of essential medicines (88.47%) as major challenges. The budget analysis reveals an average government healthcare fund allocation of $7.12 compared with an estimated expenditure of $82.75 per person annually. Nigeria allocates only an average of 0.37% of GDP and 4.61% of the national budget to healthcare, comprising a maximum of 13.56% of total health expenditure. This study emphasises the urgent need for policy reforms and implementations to improve Nigeria's healthcare financing and service quality. Targeted interventions are essential to address systemic challenges and meet population needs while aligning with international health services and best standards.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".