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Record W1891852936 · doi:10.1080/14999013.2015.1073196

Non-Suicidal Self-Injury in Male Offenders: Initiation, Motivations, Emotions, and Precipitating Events

2015· article· en· W1891852936 on OpenAlexaboutno aff
Jenelle Power, Amelia M. Usher, Janelle N. Beaudette

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2015
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAngerPsychologyRegretClinical psychologyPsychological interventionCoping (psychology)Suicide preventionMental healthInterpersonal communicationPsychiatryInjury preventionPoison controlSocial psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

Non-suicidal self-injury (NSSI) is a complex issue that poses a risk to offenders and staff in all correctional jurisdictions and understanding of this behaviour in adult male offenders is limited. One hundred and four federally sentenced men in Canada participated in semi-structured interviews that were designed to assess their history of NSSI. Interview questions centered on mental health and history of abuse, suicide attempts, and non-suicidal self-injury, including whether the behaviour occurred before or after admission to a correctional facility. Coping was the most common reason provided for engaging in self-injury. The second most frequently reported reason was instrumental reasons, which involved using NSSI to exert control or obtain external rewards. Among a subset of men who first initiated NSSI in a Canadian federal institution, institutional-specific interpersonal influence was the most frequent motivation. The emotions most commonly reported by the men prior to engaging in NSSI were anger and frustration, and the emotions most commonly reported following NSSI were relief, followed by regret. Given that the motivations for NSSI and the emotions experienced by the individual can vary considerably, interventions should consider these motivations and emotions.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.063
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.051
GPT teacher head0.374
Teacher spread0.323 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

Explore more

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