Intimate partner violence against adolescent girls: regional and national prevalence estimates and associated country-level factors
Notice bibliographique
Résumé
BACKGROUND: Intimate partner violence is a serious public health problem and negatively affects short-term and long-term health, development, and wellbeing of adolescent girls. Global estimates from WHO have shown that adolescent girls aged 15-19 years experience high rates of intimate partner violence. We aimed to estimate the lifetime and past-year prevalence and patterns of physical or sexual intimate partner violence against adolescent girls by male partners across 161 countries and areas, and to examine the country-level factors, including the prevalence of child marriage, associated with the lifetime and past-year prevalence of intimate partner violence in this age group. METHODS: These analyses used the 2018 global, regional, and country estimates on intimate partner violence published by WHO and economic, social, and political metadata from subject-specific databases. Drawing on data from the WHO Global Database on Prevalence of Violence Against Women, we used hierarchical Bayesian modelling techniques to estimate lifetime and past-year prevalence of physical or sexual (or both) intimate partner violence against adolescent girls aged 15-19 years by country. Linear regression methods were used to examine contextual social, economic, and political factors associated with intimate partner violence against adolescent girls in the 101 countries (lifetime prevalence) and 105 countries (past-year prevalence) for which these metadata were available. FINDINGS: The estimated global prevalence of physical or sexual intimate partner violence against ever-partnered adolescent girls aged 15-19 years was 24% (95% uncertainty interval 21-28) in their lifetime and 16% (14-19) in the past year. Prevalence varied greatly across countries and regions, with lifetime prevalence ranging from 6% (3-11) in Georgia to 49% (35-64) in Papua New Guinea. Overall, the prevalence of both lifetime (154 countries) and past-year (157 countries) intimate partner violence against adolescent girls was higher in low-income and lower-middle-income countries and regions than in high-income countries and regions. Countries with higher rates of female secondary school enrolment and those with inheritance laws that are more gender-equal had lower prevalence of intimate partner violence against adolescent girls. Lower-income countries and societies with a high prevalence of child marriage had higher prevalence of physical or sexual intimate partner violence against adolescent girls. INTERPRETATION: Our findings highlight the widespread prevalence of intimate partner violence against adolescent girls across the globe and its relationship with country-level contextual factors. They emphasise the need for promoting and ensuring policies and programmes that increase and ensure gender equality. Countries should strive to provide secondary education for all girls, ensure equal property rights for women, eliminate discriminatory gender norms, and address harmful practices such as child marriage. FUNDING: UN Women-WHO Joint Programme on Strengthening Violence Against Women Data funded by the UK Foreign and Commonwealth Development Office and the UNDP-UNFPA-UNICEF-WHO-World Bank Special Programme of Research, Development, and Research Training in Human Reproduction.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».