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Record W4399841833 · doi:10.1177/10775595241264009

Emergency Department Presentations for Injuries Following Agency-Notified Child Maltreatment: Results From the Childhood Adversity and Lifetime Morbidity (CALM) Study

2024· article· en· W4399841833 on OpenAlexaff
Mike Trott, Claudia Bull, Urška Arnautovska, Dan Siskind, Nicola Warren, Jake M. Najman, Steve Kisely

Bibliographic record

VenueChild Maltreatment · 2024
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsDalhousie University
FundersNational Health and Medical Research CouncilMetro South Health
KeywordsPoison controlMedicineSuicide preventionInjury preventionPhysical abuseOccupational safety and healthVictimisationEmergency departmentChild abusePsychiatryMedical emergency

Abstract

fetched live from OpenAlex

Child maltreatment (CM) is associated with negative health outcomes in adulthood, including deliberate self-harm (DSH), suicidal behaviours, and victimisation. It is unknown if associations extend to emergency department (ED) presentations for non-DSH related injuries. Birth cohort study data was linked to administrative health data, including ED presentations for non DSH related injuries and agency-reported and substantiated notifications for CM. Adjusted analyses ( n = 6087) showed that any type of agency-reported notification for CM was significantly associated with increased odds of ED presentation for injuries (aOR = 1.57; 95% CI 1.32–1.87). In moderation analyses, women yielded significantly higher odds of notified and substantiated physical abuse, substantiated emotional abuse, and being subject to more than one type of substantiated abuse than males. ED presentations for injuries could be a proxy for risky behaviours, disguised DSH/suicidal behaviours, or physical abuse. The consistent findings in women may point to victimisation via interpersonal violence.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.311
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2024
Admission routes1
Has abstractyes

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