National health insurance enrolment among elderly ghanaians: the role of food security status
Bibliographic record
Abstract
Older people with food insecurity in Ghana are often exposed to poor health conditions, highlighting the importance of the National health Insurance Scheme (NHIS) enrolment for ensuring they receive necessary medical attention through access to health care services. However, we know very little about the association between food insecurity and National Health Insurance Scheme enrolment among older people in Ghana. To address this void in the literature, this study uses a representative survey of adults aged 60 or older from three regions in Ghana (i.e., Upper West, Bono, and Greater Accra regions (n = 1,073)). We find that 77% of older adults reported not being enrolled into the NHIS. Results from logistic regression analysis show that older people who experienced severe household food insecurity were less likely to enroll in the National Health Insurance Scheme than those who did not experience any food insecurity (OR = 0.48 p < 0.001). Based on these findings, we argue that in addition to the traditional socioeconomic factors, addressing severe food insecurity may improve health insurance enrolment among older adults. Additionally, policymakers should also consider older people's socioeconomic circumstances when formulating policies for them to enrol in health insurance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".