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Record W4398784894 · doi:10.1016/j.psycom.2024.100174

Youth self-harm and the role of reasons for living and hope: A secondary analysis from a randomized controlled trial

2024· article· en· W4398784894 on OpenAlexaff
Sarina Rain, Marissa Williams, Rachel Mitchell, Rabia Zaheer, Craig J. Bryan, Ayal Schaffer, Vera Yu Men, Neal Westreich, Janet Ellis, Benjamin I. Goldstein, Amy Cheung, Steven Selchen, Mark Sinyor

Bibliographic record

VenuePsychiatry Research Communications · 2024
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsCentre for Addiction and Mental HealthSunnybrook Health Science CentreUniversity of TorontoChild, Adolescent and Family Mental HealthHealth Sciences CentreQueen's University
Fundersnot available
KeywordsRandomized controlled trialHarmPsychologyClinical psychologyPoison controlSuicide preventionLogistic regressionMedicineInjury preventionEnvironmental healthInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

Self-harm (SH) is one of the strongest predictors of eventual death by suicide. This study examines the potential protective role of reasons for living (RFL) and hope in youth with a history of self-harm using data from a randomized control trial (RCT) of brief cognitive behavioural therapy (BCBT). A single-blind, pilot RCT examined the efficacy of BCBT for suicide prevention versus an attentional control in youth aged 15-25 admitted to hospital following self-harm. Subjects’ reasons for living and hope were measured weekly by the Reasons for Living Scale (RFL) and Adult Hope Scale (AHS), respectively, for 10 weeks of acute treatment. Logistic regression was performed to evaluate whether baseline RFL and AHS scores predicted repeat self-harm. Mann-Whitney U tests were used to compare median RFL and AHS scores. Our study did not find associations between reasons for living or hope and repeat self-harm in youth. Treatment with BCBT was also not associated with improved scores on either measure.

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)0.008
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.581
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.398
Teacher spread0.346 · 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 designRandomized trial
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
Published2024
Admission routes1
Has abstractyes

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