The influence of gender and household headship on voluntary health insurance: the case of North-West Cameroon
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».