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Enregistrement W4386203835 · doi:10.1186/s12905-023-02611-w

Gender differences in the acceptance of wife-beating: evidence from 30 countries in Sub-Saharan Africa

2023· article· en· W4386203835 sur OpenAlexaff
Jones Arkoh Paintsil, Kenneth Setorwu Adde, Edward Kwabena Ameyaw, Kwamena Sekyi Dickson, Sanni Yaya

Notice bibliographique

RevueBMC Women s Health · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueIntimate Partner and Family Violence
Établissements canadiensUniversity of OttawaGlobal Affairs Canada
Organismes subventionnairesnon disponible
Mots-clésWifeDemographyPsychologyMedicineGender studiesSocioeconomicsSociologyPolitical scienceLaw

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: The World Health Organization (WHO) has cited domestic violence as an urgent global maternal and child health priority. Gender differences in the acceptance of wife-beating have not been explored at the multi-country level in sub-Saharan Africa (SSA) where the occurrence of wife-beating (36%) is greater than the global average (30%). It is against this backdrop that we examine the gender differences in the acceptance of wife beating in SSA. METHODS: We used Demographic and Health Survey data from 30 SSA countries. Acceptance of wife beating among women and men was the principal outcome variable of interest. We employed Multiple correspondence analysis and logistic regression model as the primary estimation methods for this study. The descriptive statistics show that women had a higher acceptance rate (44%) of wife beating than men (25%). For the women sample, Mali, Democratic Republic of Congo, Chad, and Guinea had higher rates of acceptance of the wife beating (80.6%, 78.4%, 77.1% and 70.3% respectively) For the men, only Guinea had an acceptance rate above 50 percent. RESULTS: We found that all else equal, women's acceptance of wife beating is higher for male-headed households than for female-headed households. Women without formal education were 3.1 times more likely to accept wife beating than those with higher education. Men with no formal education were 2.3 times more likely to accept wife beating than men with higher education. We found that polygamous women were comparable to polygamous men. Polygamous women were 1.2 times more likely to accept wife beating than those in monogamous marriages. Women were 1.2 times more likely to accept wives beating if they had extramarital relationships. Contrarily, men who engaged in extramarital affairs were 1.5 times more likely to justify wife beating. We also found that women's acceptance of wife beating decreases as they age. Men who decide on major household purchases and spending decisions on their earnings are more likely to accept wife beating. Corollary, women with similar gender and employment roles also accept wife beating. Finally, exposure to mass media is significantly associated with lower acceptance of wife beating for women and men. CONCLUSION: We conclude that women have a higher acceptance rate of wife beating than men in SSA. Acceptance of wife beating differs significantly by country. Given the same level of education, women are more likely to accept wife beating than men. If women and men have similar levels of employment and gender roles, acceptance of wife beating is higher when men make major household purchasing decisions and and it is still higher even when the woman makes these decisions. Acceptance of wife beating is higher among young women and men, the uneducated, those in polygamous marriages, women, and men who engage in extra marital affairs, poor households and in rural areas. The findings indicate the need for policies and programs by SSA countries to truncate the high acceptance rate of wife beating, especially among women.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,302
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,151
Tête enseignante GPT0,380
Écart entre enseignants0,229 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations20
Publié2023
Routes d'admission1
Résumé présentoui

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