Does access to and use of primary healthcare services influence health insurance uptake? A mixed-methods study in the Wa Municipality, Ghana
Bibliographic record
Abstract
Purpose This study explored access to quality primary healthcare services under Ghana's National Health Insurance Scheme (NHIS), its impact on NHIS enrolment, and the implications for attaining Universal Health Coverage (UHC).Methods A sequential explanatory mixed-method research design was employed using a multistage sampling technique. We collected data from 413 insured individuals and 47 healthcare facilities for quantitative community-level analysis using questionnaires. Purposive sampling was used to select 17 healthcare providers and 20 insured key informants for qualitative investigation using interview guides. Quantitative data were analyzed using descriptive statistics, independent t-tests, and binary logistic regression. Qualitative data were thematically analyzed to explain the quantitative findings.Results The results indicated a positive association between outpatient and inpatient services, maternal healthcare, and laboratory testing. Maternal healthcare and laboratory diagnostics positively influenced the renewal of NHIS membership. Binary logistic regression revealed that access to, and utilization of PHC services significantly increased the likelihood of NHIS membership renewal, influenced by healthcare system factors such as service availability, acceptability, and affordability.Conclusion Our findings suggest that policymakers must enhance the NHIS with improved service delivery to attain UHC in Ghana.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".