The influence of gender and household headship on voluntary health insurance: the case of North-West Cameroon
Bibliographic record
Abstract
Within the existing health financing literature, males are typically categorized as the household's decision-makers. While this view accurately reflects many local sociocultural realities, approximately a quarter of sub-Saharan African households are now headed by females. In light of various efforts to expand health insurance coverage in the region, it is necessary to examine whether the factors influencing voluntary health insurance enrolment are analogous across male- and female-headed households. This study sought to identify the gendered determinants of voluntary enrolment into a church-run micro health insurance scheme. A cross-sectional survey of 550 households was carried out in Bui and Donga-Mantung Divisions of North-West Cameroon in May 2016. A structured questionnaire was administered on health insurance membership, household attributes, headship characteristics and health-seeking behaviour. We assessed the influence of gender on the associations between health insurance enrolment and the explanatory variables using logistic regression. This study found that voluntary health insurance demand was influenced by involvement in social networks regardless of gender. However, in line with entrenched household roles, men's understanding of potential household health risks ultimately facilitated their enrolment decisions, while economically empowered women prioritised their direct knowledge of household health risks. Men's demand for health insurance was correlated primarily with their education level (OR = 2.238 [CI 1.228-2.552]), as well as with their socioeconomic status (OR = 2.207 [CI 1.173-4.153]), age (OR = 2.238 [CI 1.151-4.352]) and trust of the insurance provider (OR = 4.770 [CI 2.407-9.453]). Conversely, women's enrolment decision was primarily associated with their income levels (OR = 5.842 [CI 1.589-21.484]), as well as by the presence of children (OR = 3.734 [CI 1.228-11.348]). The influence of wealth on health insurance enrolment highlights the need for policymakers to subsidize health insurance schemes for vulnerable population groups. Further, it is imperative to develop sensitization campaigns that are simple and digestible to facilitate understanding of health insurance across all target groups.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".