An Integrative Examination of Childhood Multiple Victimization through Ecological Lenses
Notice bibliographique
Résumé
The landscape of the childhood victimization literature is shifting, with a growing number of researchers emphasizing the importance of designing studies that account for and aim to disentangle the interconnections among victimization experiences. This is a notable contrast to the bulk of the scientific inquiry to date, which has tended to examine victimization experiences in isolation from one another and has created victimization-specific models of risk. On the other hand, the multiple victimization field aims to better understand the overlap among risk factors and the co-occurrence across victimization experiences to create general or non-specific risk models for childhood victimization. From this field emerged the concept of multiple victimization (defined as exposure to more than one type of victimization within a specified time period), that has been established as the unfortunate norm among victimized children. The current dissertation was designed not only to help attain a better understanding of the phenomenon of childhood multiple victimization but also to contribute to our understanding of the frequency, co-occurrence, and risk (grounded in the ecological framework) of childhood multiple victimization. This dissertation addresses important shortcomings of the published literature, such as the scarcity of studies that account for the co-occurrence among victimization experiences, the limited victimization data on school-aged children and clinical samples, and the dearth of studies that test comprehensive risk models of multiple victimization. Caregivers of school-aged children (N = 213) in the Ottawa/Gatineau area participated in the online study, which involved the completion of a 30-minute questionnaire package that assessed their child’s victimization experiences as well as child (e.g., sex, age), family (e.g., caregiver psychosocial functioning, family functioning), and neighbourhood (e.g., safety) factors. Results provided support for the ubiquitous nature of childhood multiple victimization (in the past year and lifetime) as well as for the common co-occurrence of various victimization experiences. Specifically, while a certain overlap was found across all victimization forms, conventional crimes and peer and/or sibling victimization co-occurred most often in this school-aged sample. In addition, victimization forms that may be qualified as “severe” (sexual victimization, Internet victimization, maltreatment) tended to co-occur with many additional forms and were rarely reported on their own. Findings highlighted the important associations between victimization exposure and psychosocial difficulties (anxiety, depression, aggression, and posttraumatic stress), and weighting techniques (i.e., weighting severe victimization forms more heavily) were not found to significantly contribute to better predictability of psychosocial difficulties. Turning to the risk models, a number of correlates of childhood multiple victimization were identified, most notably family variables including family dysfunction, caregiver psychosocial functioning, and substance use problems. However, a number of correlates (particularly socio-demographic factors) were also found to vary according to the victimization experiences assessed, providing partial support for the specificity assumption whereby victimization risk models vary according to the victimization form assessed. The theoretical and applied implications of research findings for efforts aimed at addressing childhood multiple victimization were also discussed.
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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 | 0,005 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,012 |
| Communication savante | 0,009 | 0,009 |
| Science ouverte | 0,002 | 0,010 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».