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Record W2888778556 · doi:10.1111/sltb.12510

Experiencing and Resisting Nonsuicidal Self‐injury Thoughts and Urges in Everyday Life

2018· article· en· W2888778556 on OpenAlexaff
Brianna J. Turner, J. Sebastian Baglole, Alexander L. Chapman, Kim L. Gratz

Bibliographic record

VenueSuicide and Life-Threatening Behavior · 2018
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsCarleton UniversitySimon Fraser UniversityUniversity of Victoria
Fundersnot available
KeywordsEveryday lifePsychologyPsychotherapistPsychoanalysisPolitical scienceLaw

Abstract

fetched live from OpenAlex

OBJECTIVE: We used a daily diary to examine (1) the frequency of three types of NSSI thoughts and urges (fleeting thoughts, persistent thoughts, and intense urges), (2) the correlates of NSSI thought and urges within and across days, (3) strategies that aid in resisting NSSI thoughts and urges, and (4) the prospective association of daily NSSI thoughts and urges with NSSI behavior over 12 months. METHOD: Sixty adults (aged 18-35) completed a two-week daily diary and follow-ups every 3 months for one year. RESULTS: Fleeting NSSI thoughts were reported on 48% of days, whereas persistent thoughts (11%) and intense urges (17%) were less common. Within days, earlier stress predicted more persistent NSSI thoughts, whereas earlier perceived support predicted less intense NSSI urges. Furthermore, NSSI thoughts and urges were positively associated with same-day stress and avoidant coping. Problem-focused coping was associated with greater success resisting same-day fleeting thoughts and intense urges, as well as success resisting next-day persistent thoughts. Perceived support was associated with less intense same-day urges, but also less success resisting these urges. Over the follow-up, persistent thoughts predicted less frequent NSSI, whereas intense urges predicted more frequent NSSI. CONCLUSIONS: NSSI thoughts and urges are commonly experienced and deserve further attention.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.043
GPT teacher head0.340
Teacher spread0.297 · 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

Citations61
Published2018
Admission routes1
Has abstractyes

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