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Record W2340532422 · doi:10.1037/abn0000104

The link between nonsuicidal self-injury and acquired capability for suicide: A longitudinal study.

2015· article· en· W2340532422 on OpenAlexafffundabout
Teena Willoughby, Taylor Heffer, Chloe A. Hamza

Bibliographic record

VenueJournal of Abnormal Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyLongitudinal studyPoison controlHuman factors and ergonomicsSuicide preventionInjury preventionClinical psychologySuicide attemptOccupational safety and healthMedicineMedical emergency

Abstract

fetched live from OpenAlex

Despite recent findings that nonsuicidal self-injury (NSSI) is a strong predictor of suicide attempts, little empirical attention has been given to the mechanism through which NSSI increases suicide risk. The present 2-wave longitudinal study represents the first critical test of Joiner's (2005) hypothesis that NSSI is linked to lower pain sensitivity and fear of death over time (i.e., NSSI leads to acquired capability for suicide). Undergraduate students (N = 782) at a midsized Canadian university completed measures of NSSI and acquired capability for suicide at 2 time points (1 year apart). Path analyses revealed that higher frequency of NSSI engagement in the past year was associated with greater acquired capability for suicide 1 year later, and that this link was unidirectional. This study provides the first longitudinal evidence that a potential mechanism for the link between NSSI and suicide attempts may be acquired capability for suicide, and suggests that targeting NSSI engagement could help to prevent individuals from acquiring the ability to enact more lethal forms of self-injury.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.093
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.103
GPT teacher head0.410
Teacher spread0.307 · 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".

Quick stats

Citations139
Published2015
Admission routes3
Has abstractyes

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