Barriers to accessing healthcare among women in Ghana: a multilevel modelling
Notice bibliographique
Résumé
BACKGROUND: Women's health remains a global public health concern, as enshrined in the Sustainable Development Goals. This study, therefore, sought to assess the individual and contextual factors associated with barriers to accessing healthcare among women in Ghana. METHODS: The study was conducted among 9370 women aged 15-49, using data from the 2014 Ghana Demographic and Health Survey. Barrier to healthcare, derived from four questions- whether a woman faced problems in getting money, distance, companionship, and permission to see a doctor-was the outcome variable. Descriptive and multilevel logistic regression analyses were carried out. The fixed effect results of the multilevel logistic regression analyses were reported using adjusted odds ratios at a 95% confidence interval. RESULTS: More than half (51%) of the women reported to have at least one form of barrier to accessing healthcare. Women aged 45-49 (AOR = 0.65, CI: 0.49-0.86), married women (AOR = 0.71, CI:0.58-0.87), those with a higher level of education (AOR = 0.51, CI: 0.37-0.69), those engaged in clerical or sales occupation (AOR = 0.855, CI: 0.74-0.99), and those who were covered by health insurance (AOR = 0.59, CI: 0.53-0.66) had lower odds of facing barriers in accessing healthcare. Similarly, those who listened to radio at least once in a week (AOR =0.77, CI: 0.66-0.90), those who watched television at least once a week (AOR = 0.75, CI: 0.64-0.87), and women in the richest wealth quintile (AOR = 0.47, CI: 0.35-0.63) had lower odds of facing barriers in accessing healthcare. However, women who were widowed (AOR = 1.47, CI: 1.03-2.10), those in the Volta Region (AOR 2.20, CI: I.38-3.53), and those in the Upper West Region (AOR =2.22, CI: 1.32-3.74) had the highest odds of facing barriers to healthcare accessibility. CONCLUSION: This study shows that individual and contextual factors are significant in predicting barriers in healthcare access in Ghana. The factors identified include age, marital status, employment, health insurance coverage, frequency of listening to radio, frequency of watching television, wealth status, and region of residence. These findings highlight the need to pay critical attention to these factors in order to achieve the Sustainable Development Goals 3.1, 3.7, and 3.8. It is equally important to strengthen existing strategies to mitigate barriers to accessing healthcare among women in Ghana.
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Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 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 ».