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Record W4205774310 · doi:10.1097/nmd.0000000000001481

Support Seeking in the Context of Self-Injury Recovery

2022· article· en· W4205774310 on OpenAlexaff
Saha Meheli, Stephen P. Lewis

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2022
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEmbarrassmentShameThematic analysisIntrapersonal communicationPsychologyContext (archaeology)Help-seekingOutreachInterpersonal communicationCoachingAgency (philosophy)Social psychologyApplied psychologyPsychotherapistMedical educationQualitative researchMedicineMental healthPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT: A sample of 229 university students responded to open-ended questions for the present study, which aimed to gain a deeper understanding of lived experiences of support seeking in the context of self-injury recovery. Inductive thematic analysis of the responses indicated themes from two domains: the benefits of support seeking and the barriers to support seeking. The first domain highlighted benefits from both professional sources (such as receiving diagnosis and referrals to therapy, learning emotion regulation strategies, and developing an improved understanding of self) and informal sources (such as receiving tangible aid, having a support system, and having a compassionate space). The second domain indicated that barriers could be both intrapersonal (such as desire to continue nonsuicidal self-injury, embarrassment and shame, establishing agency without others' support, and minimizing self-injury) and interpersonal (such as fear of being stigmatized, concern for others, and unhelpful prior experiences) in nature. Implications for future research, outreach efforts, and clinical practice 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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.020
GPT teacher head0.295
Teacher spread0.276 · 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 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

Citations16
Published2022
Admission routes1
Has abstractyes

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