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Record W4206772868 · doi:10.1108/jpmh-07-2021-0089

Correlates of disclosure of non-suicidal self-injury amongst Australian university students

2022· article· en· W4206772868 on OpenAlexaff
Sylvanna Mirichlis, Penelope Hasking, Stephen P. Lewis, Mark Boyes

Bibliographic record

VenueJournal of Public Mental Health · 2022
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Guelph
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsIntrapersonal communicationPsychologyClinical psychologyDistressMental healthSelf-disclosureSuicide preventionCognitionPopulationPoison controlPsychiatryMedicineSocial psychologyInterpersonal communicationMedical emergency

Abstract

fetched live from OpenAlex

Purpose Non-suicidal self-injury (NSSI) is associated with psychological disorders and suicidal thoughts and behaviours; disclosure of NSSI can serve as a catalyst for help-seeking and self-advocacy amongst people who have self-injured. This study aims to identify the socio-demographic, NSSI-related, socio-cognitive and socio-emotional correlates of NSSI disclosure. Given elevated rates of NSSI amongst university students, this study aimed to investigate these factors amongst this population. Design/methodology/approach Australian university students ( n = 573) completed online surveys; 80.2% had previously disclosed self-injury. Findings NSSI disclosure was associated with having a mental illness diagnosis, intrapersonal NSSI functions, specifically marking distress and anti-dissociation, having physical scars from NSSI, greater perceived impact of NSSI, less expectation that NSSI would result in communication and greater social support from friends and significant others. Originality/value Expanding on previous works in the area, this study incorporated cognitions about NSSI. The ways in which individuals think about the noticeability and impact of their NSSI, and the potential to gain support, are associated with the decision to disclose self-injury. Addressing the way individuals with lived experience consolidate these considerations could facilitate their agency in whether to disclose their NSSI and highlight considerations for health-care professionals working with clients who have lived experience of NSSI.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.347
Teacher spread0.314 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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