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Record W2810862922 · doi:10.1080/09515070.2018.1489220

Barriers and responses to the disclosure of non-suicidal self-injury: a thematic analysis

2018· article· en· W2810862922 on OpenAlexaff
Shaina A. Rosenrot, Stephen P. Lewis

Bibliographic record

VenueCounselling Psychology Quarterly · 2018
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsShameThematic analysisPsychologySelf-disclosureSilenceClinical psychologySuicide preventionHuman factors and ergonomicsPoison controlQualitative researchSocial psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

Non-suicidal self-injury (NSSI) is a prevalent behaviour among youth and young adults, yet little is known about their NSSI disclosure experiences. Disclosure of NSSI may have important implications for accessing treatment and eliciting support from family and friends; it may also highlight where efforts are needed to combat potential barriers (e.g. shame). This study sought to better understand the factors that facilitate and discourage NSSI disclosure in a sample of undergraduate students as well as gather a richer understanding of young adults’ experiences disclosing NSSI. To do this, a thematic analysis of interview transcripts with 17 students (16 women and 1 man) was conducted. Themes related to barriers to disclosure (shame, concern about others) and disclosure recipients’ responses (silence/avoidance, understanding) were explored. Results underscored the central role of shame in NSSI disclosures, both as an experience impacting the difficulty and likelihood of disclosure and as a potential consequence of receiving avoidant responses to disclosure. Among the clinical implications discussed is the import of initiatives to reduce NSSI stigma and foster supportive and understanding responses to NSSI disclosures.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.350
Teacher spread0.330 · 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

Citations103
Published2018
Admission routes1
Has abstractyes

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