Understanding unmet health-care need among older Ghanaians: a gendered analysis
Bibliographic record
Abstract
Abstract Health insurance schemes are important for bridging gaps in health-care needs between the rich and poor, especially in contexts where poverty is higher among seniors (persons aged 65 years and above). In this study we examined (a) gender-based predictors of unmet health-care need among seniors and (b) whether access was influenced by wealth status (measured by income quintiles). Gender-specific negative log–log regression models were fitted to data from the Study on Global Ageing and Health to examine associations between unmet health-care need and health insurance status controlling for theoretically relevant covariates. Insurance status was an important determinant of men and women's unmet health-care need but the relationship was moderated by income quintile for women and not men. While occupation was important for men, religion, marital status and income quintile were significantly associated with women's unmet health-care need. Based on the observed gender differences, we recommend the implementation of programmes aimed at improving the economic situation of older people, particularly women.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".