The Effects of Social Capital and Neighborhood Characteristics on Intimate Partner Violence: A Consideration of Social Resources and Risks
Notice bibliographique
Résumé
Intimate partner violence (IPV) is a growing public health problem, and gaps exist in knowledge with respect to appropriate prevention and treatment strategies. A growing body of research evidence suggests that beyond individual factors (e.g., socio-economic status, psychological processes, substance abuse problems), neighborhood characteristics, such as neighborhood economic disadvantage, high crime rates, high unemployment and social disorder, are associated with increased risk for IPV. However, existing research in this area has focused primarily on risk factors inherent in neighborhoods, and has failed to adequately examine resources within social networks and neighborhoods that may buffer or prevent the occurrence of IPV. This study examines the effects of neighborhood characteristics, such as economic disadvantage and disorder, and individual and neighborhood resources, such as social capital, on IPV among a representative sample of 2412 residents of Toronto, Ontario, Canada. Using a population based sample of 2412 randomly selected Toronto adults with comprehensive neighborhood level data on a broad set of characteristics, we conducted multi-level modeling to examine the effects of individual- and neighborhood-level effects on IPV outcomes. We also examined protective factors through a comprehensive operationalization of the concept of social capital, involving neighborhood collective efficacy, community group participation, social network structure and social support. Findings show that residents who were involved in one or more community groups in the last 12 months and had high perceived neighborhood problems were more likely to have experienced physical IPV. Residents who had high perceived social support and low perceived neighborhood problems were less likely to experience non-physical IPV. These relationships did not differ by neighborhood income or gender. Findings suggest interesting contextual effects of social capital on IPV. Consistent with previous research, higher levels of perceived neighborhood problems can reflect disadvantaged environments that are more challenged in promoting health and regulating disorder, and can create stressors in which IPV is more likely to occur. Such analyses will be helpful to further understanding of the complex, multi-level pathways related to IPV and to inform the development of effective programs and policies with which to address and prevent this serious public health issue.
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 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 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».