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Functions of nonsuicidal self-injury and repeated nonsuicidal self-injury among adolescents: A moderating role of addictive features

2024· article· en· W4396835788 on OpenAlexaboutno aff
Dan Luo, Xia Huang, Mingfeng Lai, Hongxia Shi, Jing Li, Xiacan Chen, Jiajun Xu, Wanjun Guo

Bibliographic record

VenueJournal of Psychiatric Research · 2024
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAddictionAddictive behaviorClinical psychologyPsychologyPoison controlInjury preventionHuman factors and ergonomicsRepeated measures designPsychiatryDevelopmental psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: The high prevalence and addictive features of nonsuicidal self-injury (NSSI) in adolescents have been documented, but the role of addictive features in the process from NSSI functions to behaviour remains unclear. The major aim of this study was to investigate the effect of addictive features on NSSI functions and the severity of repeated NSSI. METHODS: A total of 10,781 students from primary and middle schools in Chengdu and Karamay were invited to participate in the online cross-sectional survey, and 10,501 completed the survey. Two self-report questionnaires, the Ottawa Self-Injury Inventory (OSI) and the Adolescent Self-Harm Scale (ASHS), were used to collect data from all participants. RESULTS: Among the students, 23.45% and 6.64% reported having engaged in NSSI at least once or at least five times in the past year. Being a girl, being an only child, and being in a single-parent family were significantly associated with more severe NSSI. Addictive features have high value for predicting repeated NSSI. In addition to their significant independent/direct additive effects, addictive features mediated and moderated the relationship between NSSI functions and increased severity of NSSI in adolescents. DISCUSSION AND CONCLUSIONS: The findings suggest that addictive features play a critical role in the development of repeated NSSI in adolescents, which indicates that addiction models may partially explain the mechanism underlying increased severity of NSSI. This may enhance understanding of the reasons for repeated NSSI and inform interventions for repeated NSSI among adolescents.

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)0.003
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 score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.021
GPT teacher head0.360
Teacher spread0.339 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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