Multiple Types of Child Maltreatment and Adolescent Mental Health in Viet Nam/Maltraitance Infantile Multiple et Sante Mentale De L'adolescent Au Viet Nam/ Maltrato Multiple De Ninos Y Salud Mental del Adolescente En Viet Nam
Notice bibliographique
Résumé
Introduction Research into the prevalence and consequences of child abuse and neglect has been changing in focus in recent years. In the past, most community-based and clinical studies assessed a single type or at most two types of maltreatment. However, it is now becoming clear that particular types of abuse seldom occur alone. A significant proportion of young people questioned in community-based surveys in Australia, Canada, Israel and the United States of America (USA), report having suffered multiple types of maltreatment (MTM) as children. (1-7) When adolescents and adults who have experienced MTM are compared with those who have suffered only a single type of maltreatment, MTM victims usually have substantially more mental health and behavioural problems. (8-11) The evidence supports an additive model of maltreatment effects. (2) A further significant development in research has been an exploration of the role of the individual, family and social contexts in which victimization occurs. (12) Social ecology models recognize that maltreatment is but one childhood experience among many that can affect mental health later in life. This approach is well illustrated by a recent national survey of children and adolescents in the USA. Turner et al. (11) considered a spectrum of cultural, socioeconomic and family influences on well-being, in addition to specific types of child maltreatment. Hierarchical regression analysis revealed that background factors contributed significantly to variance in mental health, independently of maltreatment, neglect and other forms of non-victimization trauma and adversity (such as exposure to serious physical illness, accidents or parental substance abuse). A compelling conclusion is that research into statistical linkages between child maltreatment and health may be confounded unless causal factors associated with both are included in multivariate models. (11,12) These approaches to child maltreatment research have rarely been applied in developing countries. One study in Turkey of 862 school students aged 14-17 years examined potential associations between four forms of abuse and neglect and suicide attempts and self-mutilation. (13) Logistic regression revealed a linear association between each form of maltreatment and both harmful behaviours, but that study did not adjust for social and family influences. In Thailand, a small survey of 202 young adult residents of Bangkok found an association between common mental disorders and background family conditions (low parental education, domestic violence) as well as childhood emotional and sexual abuse, with the latter two factors remaining significantly associated with mental disorders after adjustment for family characteristics. (14) To date, no descriptive or analytical research into child maltreatment in Viet Nam has been published in peer-reviewed journals. In Asia little research has been conducted on the relative health effects of social contexts and childhood adversity, broadly defined. Extrapolation of the insights gained in non-Asian countries is problematic because the prevalence of specific acts of violence, their perpetrators and the social settings in which abuse occurs vary among countries and cultures. (15-18) Indeed, considerable differences appear to exist between Asia and other regions in the frequency of child sexual abuse and intrafamilial or school-based physical abuse. (19-22) The factors that potentiate child abuse and place children at risk of poor mental health might not be generalizable across cultures. For example, some of the main risk factors for both child abuse and mental distress in children in economically developed nations, such as parental divorce, step-parenting, maternal alcohol and drug abuse and neighbourhood violence, (4,7) are comparatively rare in Asian countries. In addition, researchers in countries with multicultural populations, particularly in North America, often emphasize ethnic and cultural differences, (22,23) yet this source of variation may be less influential in comparatively monoracial societies, such as China, Japan and Viet Nam. …
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».