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Enregistrement W4385851626 · doi:10.1001/jamanetworkopen.2023.29172

Mental Illness Following Physical Assault Among Children

2023· article· en· W4385851626 sur OpenAlexafffundabout
Étienne Archambault, Simone N. Vigod, Hilary K. Brown, Hong Lu, Kinwah Fung, Michelle Shouldice, Natasha Saunders

Notice bibliographique

RevueJAMA Network Open · 2023
Typearticle
Langueen
DomainePsychology
ThématiqueChild Abuse and Trauma
Établissements canadiensHospital for Sick ChildrenPublic Health OntarioThe Scarborough HospitalInstitute for Clinical Evaluative SciencesCentre Hospitalier Universitaire Sainte-JustineWomen's College HospitalUniversity of Toronto
Organismes subventionnairesUniversity of Toronto ScarboroughSickkids Research InstituteWomen's College HospitalDepartment of Psychiatry, University of TorontoHospital for Sick ChildrenUniversity of Toronto
Mots-clésMental illnessPsychiatryMedicinePsychologyMental health

Résumé

récupéré en direct d'OpenAlex

Importance: Physical assault during childhood is common and can lead to lasting mental health problems. Yet, there are few studies on the patterns of mental illness (ie, timing of onset, type, and acuity) in survivors of physical assault. Objective: To determine the risk of incident health record diagnoses of mental illness among children who experienced assault compared with children who did not. Design, Setting, and Participants: This population-based matched cohort study used linked health administrative data sets in Ontario, Canada. Children aged 0 to 13 years who experienced an incident physical assault between 2006 and 2014 were age-matched (1:4) to children who had not experienced assault and followed up for a minimum of 5 years. Data were analyzed from January 2020 to March 2022. Exposure: Physical assault resulting in hospitalization or an emergency department (ED) visit between the ages of 0 and 13 years. Main Outcomes and Measures: The primary outcome was incident health record diagnosis of mental illness measured as any physician or hospital mental health care use or completed suicide. Secondary outcome measures included the acuity of incident mental illness and mental illness diagnostic category. Cox proportional hazards regression analysis generated hazard ratios (HR) for incident mental illness. Results: A total of 21 948 children unexposed to assault and 5487 exposed to assault were included in the study with a mean (SD) age of 7.0 (4.6) years. There were more boys in the group that experienced assault (3006 individuals [54.8%]) compared with the group who did not (9909 individuals [45.1%]). Compared with children unexposed to assault, those exposed were more likely to be in the highest deprivation index quintile (standardized difference, 0.21) and live in rural areas (standardized difference, 0.48). Their mothers more often had active mental illness (standardized difference, 0.35). More than one-third of the exposed children had a health record diagnosis of mental illness (2219 children [38.6%]; incidence rate (IR), 53.3 per 1000 person-years) compared with 23.4% (5130 children; IR, 32.2 per 1000 person-years) of unexposed children, with an overall adjusted hazard ratio (aHR) of 1.96 (95% CI, 1.85-2.08). The greatest risk was observed in the first year following the assault (aHR, 3.08; 95% CI, 2.68-3.54). In both groups, nonpsychotic disorders were the most common type of mental illness. Initial mental illness diagnoses occurred in an acute care setting for 14.0% of exposed children (769 children) vs 2.8% of unexposed children (609 children). Conclusions and Relevance: In this population-based matched cohort study, children who experienced assault had, on average, a 2 times higher risk of receiving a mental illness diagnosis and were more likely than children who had not experienced assault to present to acute care for mental illness. Early intervention to support mental health of assaulted children is warranted, particularly in the first year following assault.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,457
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,004

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.

Tête enseignante Opus0,021
Tête enseignante GPT0,325
Écart entre enseignants0,304 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2023
Routes d'admission3
Résumé présentoui

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