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Record W7074106728

Antecedents of hospital admission for deliberate self-harm from a 14-year follow-up study using data-linkage

2010· article· en· W7074106728 on OpenAlexaff

Bibliographic record

VenueCDU eSpace Institutional Repository (Charles Darwin University) · 2010
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsCentre for Global Health Research
FundersHealthway
KeywordsLicenseeLicenseWork (physics)AttributionUniversity hospitalHospital admissionMental healthPsychiatric hospital
DOInot available

Abstract

fetched live from OpenAlex

\n \t\t\tBackground: A prior episode of deliberate self-harm (DSH) is one of the strongest predictors of future completed suicide. Identifying antecedents of DSH may inform strategies designed to reduce suicide rates. This study aimed to determine whether individual and socio-ecological factors collected in childhood and adolescence were associated with later hospitalisation for DSH.Methods: Longitudinal follow-up of a Western Australian population-wide random sample of 2,736 children aged 4-16 years, and their carers, from 1993 until 2007 using administrative record linkage. Children were aged between 18 and 31 years at end of follow-up. Proportional hazards regression was used to examine the relationship between child, parent, family, school and community factors measured in 1993, and subsequent hospitalisation for DSH.Results: There were six factors measured in 1993 that increased a child's risk of future hospitalisation with DSH: female sex; primary carer being a smoker; being in a step/blended family; having more emotional or behavioural problems than other children; living in a family with inconsistent parenting style; and having a teenage mother. Factors found to be not significant included birth weight, combined carer income, carer's lifetime treatment for a mental health problem, and carer education.Conclusions: The persistence of carer smoking as an independent risk factor for later DSH, after adjusting for child, carer, family, school and community level socio-ecological factors, adds to the known risk domains for DSH, and invites further investigation into the underlying mechanisms of this relationship. This study has also confirmed the association of five previously known risk factors for DSH.\n

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.244
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.269
Teacher spread0.242 · 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.

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

Citations0
Published2010
Admission routes1
Has abstractyes

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