Determinants of Health Insurance Subscription among Women of Reproductive Age in Mozambique
Bibliographic record
Abstract
Health insurance is one of the major pillars of achieving the Universal Health Coverage, as emphasized in the Sustainable Development Goals (SDGs). However, in Mozambique, although the national health system is confronted by several limitations, uptake of health insurance is not emphasized by stakeholders in the health sector. This paper therefore analysed the determinants of health insurance subscription among women of reproductive age in Mozambique. The data were the 2022/23 data for the Demographic and Health Survey (DHS) which covered 9788 women in the 15-49 years age bracket. The data were analysed with Probit regression. The results showed a very low health insurance subscription (1.69%). Additionally, 31.50% of the women with higher education had health insurance. The Probit regression results showed that the probability of insurance subscription among the women was significantly promoted (p<0.05) by access to the internet, reading newspaper, perception of good, moderate and bad health status, ownership of bank account, working, and wealth index, while residence in some regions (Inhambane, Gaza, and Sofala), and religion affiliation (Evangelical/Pentecostal and no religion) reduced it. It was concluded that in the light of prevailing constraints confronting attainment of UHC in Mozambique, there is the need to create media and internet driven advocacies to promote health insurance subscription with more focus on the wealthy, working class, urban residents and educated women.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".