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Record W4281710538 · doi:10.2147/prbm.s363433

The Effect of Emotion Regulation on Non-Suicidal Self-Injury Among Adolescents: The Mediating Roles of Sleep, Exercise, and Social Support

2022· article· en· W4281710538 on OpenAlexaboutno aff
Zhensong Lan, Pau Kee, Hapsah Md Yusof, Xuefang Huang

Bibliographic record

VenuePsychology Research and Behavior Management · 2022
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
FundersEducation Department of Guangxi Zhuang Autonomous Region
KeywordsPsychologyClinical psychologySocial supportPath analysis (statistics)Emotional regulationCognitionStructural equation modelingNegative emotionInjury preventionCognitive reappraisalDevelopmental psychologyPoison controlMedicinePsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Purpose: To explore the relationship between adolescents' emotion regulation ability and non-suicidal self-injury (NSSI) behavior and the role of sleep, exercise, and social support in this relationship. Methods: A total of 2573 adolescents were investigated with the Cognitive Emotion Regulation Scale, the Social Support Scale for Children and Adolescents, the Ottawa Self-Injury Questionnaire, and the Self-Made Living Condition Questionnaire, and path analysis was conducted based on the structural equation model (SEM). Results: 1) There was a significant correlation between emotion regulation ability and NSSI behavior; 2) sleep and social support played a mediating role between emotion regulation ability and NSSI behavior; and 3) sleep modulated the direct effect of negative emotion regulation ability on NSSI, while exercise modulated the direct effect of positive emotion regulation ability on NSSI, which indicated that sleep and exercise could effectively alleviate NSSI behavior caused by the lack of emotion regulation in 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 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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.381
Teacher spread0.351 · 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

Citations35
Published2022
Admission routes1
Has abstractyes

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