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Record W4307776424 · doi:10.1007/s10578-022-01450-4

Adolescent Inpatient Mental Health Admissions: An Exploration of Interpersonal Polyvictimization, Family Dysfunction, Self-Harm and Suicidal Behaviours

2022· article· en· W4307776424 on OpenAlexafffundabout
Shannon L. Stewart, Valbona Semovski, Natalia Lapshina

Bibliographic record

VenueChild Psychiatry & Human Development · 2022
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsWestern University
FundersPublic Health Agency of Canada
KeywordsMental healthPsychiatryMedicineSuicide preventionLogistic regressionHarmPoison controlOccupational safety and healthInpatient careIntervention (counseling)Health carePsychologyClinical psychologyMedical emergency

Abstract

fetched live from OpenAlex

Abstract The mental health system is impacted by extreme delays in the provision of care, even in the face of suicidal behaviour. The failure to address mental health issues in a timely fashion result in a dependence on acute mental health services. Improvement to the mental health care system is impacted by the paucity of information surrounding client profiles admitted to inpatient settings. Using archival data from 10,865 adolescents 12–18 years of age (Mage = 14.87, SDage = 1.77), this study aimed to examine the characteristics of adolescents admitted to psychiatric inpatient services in Ontario, Canada. Multivariate binary logistic regression revealed that adolescents reporting interpersonal polyvictimization, greater family dysfunction and higher risk of suicide and self-harm had a greater likelihood of an inpatient mental health admission. The interRAI Child and Youth Mental Health assessment can be used for care planning and early intervention to support adolescents and their families before suicide risk is imminent.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.799

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.314
Teacher spread0.276 · 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 designQualitative
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

Citations31
Published2022
Admission routes3
Has abstractyes

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