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Record W4390111885 · doi:10.1080/09515070.2023.2297883

Understanding the impact of involuntary discoveries of nonsuicidal self-injury: a thematic analysis

2023· article· en· W4390111885 on OpenAlexaff
Riley L. Pugh, Kaitlyn McLachlan, Stephen P. Lewis

Bibliographic record

VenueCounselling Psychology Quarterly · 2023
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsThematic analysisPsychologyLived experienceQualitative researchHuman factors and ergonomicsPoison controlSocial psychologyMedicinePsychotherapistSociology

Abstract

fetched live from OpenAlex

Growing research has examined instances of voluntarily disclosed nonsuicidal self-injury (NSSI), including how people with lived experience are impacted when they choose to share their NSSI with others. Voluntary disclosure, however, represents just one way that NSSI experiences become known to others; NSSI can also be discovered involuntarily, yet little to no research has explored the impact of these experiences. To understand the impact of these Involuntary Discovery Experiences (IDEs) the present study recruited 139 university students (Mage = 19.13, SD = 2.12; nfemale = 121) with lived experience of NSSI and who reported having a past IDE. Participants took part in an online study involving a series of open-ended questions concerning their past IDEs. A thematic analysis of their responses pointed to three overarching psychological impacts of IDEs: I felt Stigmatized and Marginalized, Things did not go well, and I No Longer felt Alone in my experience. These findings offer initial insights into the ways people with lived experience of NSSI may be impacted by IDEs and point to several new important research avenues. The current findings also suggest that clinicians may need to ask clients about any potential IDEs and their impact in order to best support clients who self-injure.

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 score0.330
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.097
GPT teacher head0.388
Teacher spread0.291 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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