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Record W2809130660 · doi:10.1002/jclp.22657

Understanding the social context of adolescent nonsuicidal self‐injury

2018· article· en· W2809130660 on OpenAlexaff
Sarah E. Victor, E. David Klonsky

Bibliographic record

VenueJournal of Clinical Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Mental Health
KeywordsPsychologyPsychosocialSuicidal ideationImpulsivityContext (archaeology)Clinical psychologyPoison controlSelf-destructive behaviorSuicide preventionInjury preventionHuman factors and ergonomicsDevelopmental psychologyPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: Research investigating the social context of adolescent nonsuicidal self-injury (NSSI) has been limited. We therefore examined social characteristics of NSSI, such as knowledge of friends' NSSI and the role friends play in continuing NSSI, and their relationships to other known NSSI correlates, such as suicidality. METHOD: We assessed NSSI characteristics, including social features, in a community sample of 89 self-injuring adolescents. We also assessed psychosocial correlates of NSSI, including impulsivity, self-concept, and psychiatric symptoms. RESULTS: Knowledge of friends' NSSI was relatively common among self-injurers. In addition, knowledge of friends' NSSI was associated with use of more NSSI methods, cutting behaviors, and suicidal ideation, but not with other NSSI correlates. However, teaching or encouragement of NSSI by friends was rare. CONCLUSIONS: Knowledge of friends' NSSI may serve as marker of increased severity among adolescent self-injurers. These findings have implications for identifying and intervening with high-risk self-injuring youth.

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)0.000
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.424
GPT teacher head0.542
Teacher spread0.118 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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