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Mental Illness Following Physical Assault Among Children

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

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsHospital for Sick ChildrenPublic Health OntarioThe Scarborough HospitalInstitute for Clinical Evaluative SciencesCentre Hospitalier Universitaire Sainte-JustineWomen's College HospitalUniversity of Toronto
FundersUniversity of Toronto ScarboroughSickkids Research InstituteWomen's College HospitalDepartment of Psychiatry, University of TorontoHospital for Sick ChildrenUniversity of Toronto
KeywordsMental illnessPsychiatryMedicinePsychologyMental health

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.325
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2023
Admission routes3
Has abstractyes

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