Population attributable fractions of adolescent health and well-being outcomes associated with adverse childhood experiences in a provincially representative sample in Ontario, Canada
Notice bibliographique
Résumé
It is well known that Adverse Childhood Experiences (ACEs) are associated with poor health and well-being outcomes among adult samples. However, there are notable gaps in examining these relationships among youth. The objectives were to examine: a) the prevalence of an expanded list of ACEs among adolescents, b) ACEs sex differences, c) associations between ACEs and several adolescent health and at-risk behavioural outcomes, and d) the population attributable fractions (PAFs) for three ACE groupings (i.e., child maltreatment, household challenges, and peer victimization). Cross-sectional. Data were from the provincially-representative, cross-sectional 2014 Ontario Child Health Study (N = 6,537 dwellings, response rate = 50.8%). One randomly selected child aged 14 to 17 years old (n = 2,910) from each household was included. The majority of measures (nine ACEs and six health and well-being outcomes) were self-reported (three household challenges ACEs and physical health were collected from parents/caregivers). Descriptive statistics estimated the prevalence of ACEs for the sample and by sex. Logistic regressions tested associations between individual ACEs and seven outcomes. Population attributable fractions (PAFs) were computed for three ACE groupings with each outcome. ACEs prevalence ranged from 1.8% to 47.4% with several noted sex differences. Each ACE was associated with four or more studied outcomes. PAFs ranged from 3.5% to 47.8%, varying for each ACEs grouping. The significant associations and estimated proportions of poor adolescent outcomes attributed to ACEs indicate that identifying approaches aimed at preventing these experiences could have a substantial impact on youth health and well-being. • The majority of Adverse Childhood Experiences (ACEs) were more prevalent among girls compared to boys. • Each type of ACE was related to four or more adolescent mental health and well-being outcomes. • ACEs accounted for a substantial proportion of poor adolescent health and well-being outcomes. For example, child maltreatment and household challenges accounted for approximately 1 in 3 and nearly 1 in 2 reports of suicidal ideation, respectively. Peer victimization accounted for nearly 1 in 3 mental health disorders.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».