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Record W6910299467 · doi:10.48350/186103

Correlates of child mental health and substance use related emergency department visits in Ontario: A linked population survey and administrative health data study.

2023· article· en· W6910299467 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Access CRIS of the University of Bern · 2023
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsEmergency departmentMental healthSubstance usePopulationOccupational safety and healthSubstance abusePublic healthPoison control

Abstract

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INTRODUCTION Knowledge of the sociodemographic, behavioural, and clinical characteristics of children visiting emergency departments (EDs) for mental health or substance use concerns in Ontario, Canada is lacking. OBJECTIVES Using data from a population-based survey linked at the individual level to administrative health data, this study leverages a provincially representative sample and quasi-experimental design to strengthen inferences regarding the extent to which children's sociodemographic, behavioural, and clinical characteristics are associated with the risk of a mental health or substance use related ED visit. METHODS 9,301 children aged 4-17 years participating in the 2014 Ontario Child Health Study were linked retrospectively (6 months) and prospectively (12 months) with administrative health data on ED visits from the National Ambulatory Care Reporting System. Modified Poisson regression was used to examine correlates of mental health and substance use related ED visits among children aged 4-17 years over a 12-month period following their survey completion date, adjusting for ED visits in the 6 months prior to their survey completion date. Subgroup analyses of youths aged 14-17 years who independently completed survey content related to peer victimisation, substance use, and suicidality were also conducted. RESULTS Among children aged 4-17 years, older age, parental immigrant status, internalising problems, and perceived need for professional help were statistically significant correlates that increased the risk of a mental health or substance use related ED visit; low-income and suicidal ideation with attempt were statistically significant only among youths aged 14-17 years. CONCLUSIONS Knowledge of the sociodemographic, behavioural, and clinical characteristics of children visiting EDs for mental health and substance use related concerns is required to better understand patient needs to coordinate effective emergency mental health care that optimises child outcomes, and to inform the development and targeting of upstream interventions that have the potential to prevent avoidable ED visits. HIGHLIGHTS Growing rates of child mental health and substance use related ED visits have been observed internationally.A population-based survey linked at the individual level to administrative health data was used to examine the extent to which children's sociodemographic, behavioural, and clinical characteristics are associated with the risk of a mental health or substance use related ED visit in Ontario, Canada.Older age, low-income, parental immigrant status, perceived need for professional help, internalising problems, and suicidality increase the risk of an ED visit.Knowledge of the characteristics of children visiting EDs can be used to coordinate effective emergency mental health care that optimises child outcomes, and to inform the development and targeting of upstream interventions that have the potential to prevent avoidable ED visits.

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)0.003
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 score0.023
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.162
GPT teacher head0.391
Teacher spread0.228 · 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".

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Citations0
Published2023
Admission routes1
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