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Enregistrement W3012984956 · doi:10.1371/journal.pone.0230508

Correlates of intimate partner violence among urban women in sub-Saharan Africa

2020· article· en· W3012984956 sur OpenAlexfundno aff
Chimaraoke Izugbara, Mary O. Obiyan, Anam Bhatti

Notice bibliographique

RevuePLoS ONE · 2020
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueIntimate Partner and Family Violence
Établissements canadiensnon disponible
Organismes subventionnairesInternational Development Research Centre
Mots-clésDomestic violenceDemographyPoisson regressionPopulationWifePoison controlSpouseInjury preventionGeographyMedicineEnvironmental healthSociologyPolitical science

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: The dynamics of intimate partner violence (IPV)-one of the world's leading public health problems-in urban Africa remain poorly understood. Yet, urban areas are key to the future of women's health in Africa. STUDY OBJECTIVES: We explored survivor-, partner-, and household-level correlates of prevalence rates for types of IPV in urban SSA women. METHOD: The study uses DHS data from 42,143 urban women aged 15-49 in 27 SSA countries. Associations at the bivariate level were examined using the Pearson Chi-square test. The modified Poisson regression test estimated the relative prevalence of IPV subtypes in the study population at the multivariate level. RESULTS: Approximately 36% of women in urban SSA experienced at least one form of IPV; 12.8% experienced two types; and 4.6% experienced all three types. SSA urban women who had only primary-level education, had 3 or more living children, were informally employed, were in polygynous unions, or who approved of wife-beating similarly displayed higher adjusted prevalence rates for all three forms of IPV compared respectively to their counterparts without formal education, without a living child, were unemployed, in monogamous unions, or who do not approve of wife-beating. On the other hand, the region's urban women who began cohabiting between ages 25 and 35 years or who lived in higher wealth households showed consistently lower adjusted prevalence rates for all three forms of IPV relative to their counterparts who began cohabiting before 18 years or who lived in lower wealth households. Compared to their counterparts without formal education, without a living child, or whose partners did not have formal education, women with secondary and higher education, with 1-2 living children, or whose partners had only primary level schooling displayed higher adjusted prevalence rates for both IPEV and IPPV, but not for IPSV. However, relative to their counterparts whose partners were aged 25 years or below, living with a partner aged 40 years and above was associated with statistically significant reduced prevalence rates for IPPV and IPSV, but not for IPEV. Only for IPPV did women with partners educated at secondary and above levels display statistically significant higher adjusted prevalence rates relative to their counterparts with uneducated partners. Also, solely for IPPV did women who began cohabiting between ages 18 and 24 years or whose partners were employed (whether formally or informally) show decreased adjusted prevalence rates relative to their counterparts who started cohabiting before 18 years or whose partners were unemployed. In addition, only for IPSV did women aged 40 years and above or living in middle wealth households show statistically significant reduced adjusted prevalence rates relative to their counterparts aged less than 25 years or living in lower wealth households. DISCUSSION AND CONCLUSION: By 2030, the majority of SSA women will be urban dwellers. Complexities surround IPV in urban SSA, highlighting the unique dynamics of the problem in this setting. While affirming the link between IPV and marital power inequities and dynamics, findings suggest that the specific correlates of prevalence rates for different IPV sub-types in urban SSA women can, at once, be both similar and unique. The contextual drivers of the differences and similarities in the correlates of the prevalence rates of IPV sub-types among the region's urban women need further interrogation.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,007
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,046
Tête enseignante GPT0,262
Écart entre enseignants0,215 · 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 source (Gemma direct ou Codex distillé), 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

Citations92
Publié2020
Routes d'admission1
Résumé présentoui

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