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Record W4411236176 · doi:10.1002/casp.70126

Exploring Voluntary Disclosure of Non‐Suicidal Self‐Injury: A Thematic Analysis

2025· article· en· W4411236176 on OpenAlexaff
Sylvanna Mirichlis, Stephen P. Lewis, Mark Boyes, Penelope Hasking

Bibliographic record

VenueJournal of Community & Applied Social Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Guelph
FundersMedical Research CouncilNational Health and Medical Research CouncilCurtin University of Technology
KeywordsThematic analysisThematic mapPsychologyBusinessQualitative researchSociologyGeographyCartography

Abstract

fetched live from OpenAlex

ABSTRACT Voluntary disclosure of Non‐Suicidal Self‐Injury (NSSI) refers to instances when an individual chooses to share with another person that they have self‐injured. To date, the processes involved in deciding to disclose NSSI are not well understood from a lived experience perspective. The aim of this qualitative study was to explore lived experience perspectives of the decision to voluntarily disclose NSSI. Fifteen semi‐structured interviews were conducted with university students who were aged between 18 and 25 ( M = 20.33, SD = 1.88), with 11 identifying as female. All participants had previously disclosed their NSSI to at least one other person. The interview transcripts were analysed using reflexive thematic analysis. Several themes were identified including: The Value of Trust in Disclosure, Take Context into Account, Support for one and for all, Disclosing in the face of fear, Selective Sharing, and Perceptions of Disclosure. The findings of this study highlight the multifaceted and ongoing nature of voluntary NSSI disclosure decision‐making. In particular, NSSI disclosure decision‐making was grounded within a strong value system and shaped by proximal as well as distal contextual factors. The opportunity to receive support or to help others appears of particular importance, despite significant barriers to NSSI disclosure.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.104
GPT teacher head0.401
Teacher spread0.297 · 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.

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
Published2025
Admission routes1
Has abstractyes

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