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Record W4403011228 · doi:10.7870/cjcmh-2024-015

Lived Experience Views on What Contributes to Self-Injury Stigma: A Thematic Analysis

2024· article· en· W4403011228 on OpenAlexaffvenue
Stephen P. Lewis, Joanna Collaton, Nancy L. Heath, Rob Whitley

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2024
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcGill UniversityUniversity of Guelph
Fundersnot available
KeywordsThematic analysisStigma (botany)PsychologyLived experiencePsychotherapistClinical psychologyPsychiatryQualitative researchSociology

Abstract

fetched live from OpenAlex

Growing research has sought to understand non-suicidal self-injury (NSSI) stigma. To build on this literature, we qualitatively explored what young adults with lived experience of NSSI believed contributed to its stigmatization. Participants (n = 97) were asked open-ended online questions about what they perceived as contributing to NSSI stigma, which were then analysed via reflexive thematic analysis. Resultant themes indicated that participants believe stigma stems from people, who do not self-injure, misunderstanding self-injury as attention-seeking or conflate it with suicide; they also believe that the media may play a role in this regard (e.g., glamourization, inaccurate portrayals of recovery). Findings offer initial support for a recent theoretical framework for NSSI stigma and point to several implications, which are discussed.

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)0.015
metaresearch head score (Gemma)0.022
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0050.007
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0010.002
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 head0.111
GPT teacher head0.419
Teacher spread0.308 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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