Non-suicidal self-injury behavior in adolescents： the impact of mobile phone social media dependence and related factors
Bibliographic record
Abstract
BackgroundNon-suicidal self-injury （NSSI） behaviors are prevalent among adolescents， significantly affecting their physical and mental well-being. Understanding the risk factors associated with adolescent NSSI is crucial for prevention. Previous studies have identified mobile phone dependence as a risk factor for NSSI in adolescents. However， as a key form of mobile phone dependence， the evidence regarding the impact of mobile phone social media dependence on adolescent NSSI behavior remains insufficient.ObjectiveTo explore the impact of mobile phone social media dependence and its associated factors on adolescent NSSI behavior， so as to provide references for intervention strategies targeting NSSI in adolescents.MethodsA total of 100 adolescents diagnosed with NSSI according to the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5）， and receiving treatment at Tongde Hospital of Zhejiang Province from January 2022 to December 2023 were included in the study group. Concurrently， 100 age- and sex-matched students from Hangzhou were recruited as the control group. Assessments were conducted using Ottawa Self-injury Inventory（OSI） Function Subscale and Addiction Features Subscale， Adolescents Self-Harm Scale（ASHS）， and Mobile Phone Social Media Dependence Questionnaire. Multiple linear regression was used to analyze the factors influencing NSSI behaviors.ResultsThe research group had a total of 99 patients （99.00%） who completed the study， while the control group consisted of 97 （97.00%） adolescents who finished this research.The study group had statistically significantly higher total scores on the Mobile Phone Social Media Dependence Questionnaire， as well as higher scores on the conflict and withdrawal dimensions， compared with control group（t=-3.061， -2.874， -2.368， P<0.05 or 0.01）. The study group also scored significantly higher on the OSI Function Subscale for internal emotion regulation， social influence， external emotion regulation， and sensation-seeking factors， as well as on the OSI Addiction Features Subscale scores， compared to the control group（t=-22.249， -8.854， -17.968， -10.591， -20.157， P<0.01）. OSI Function Subscale scores were positively correlated with Mobile Phone Social Media Dependence Questionnaire scores （r=0.321， P<0.01）， and OSI Addiction Features Subscale scores were positively correlated with Mobile Phone Social Media Dependence Questionnaire scores （r=0.282， P<0.01）. ASHS scores were positively correlated with Mobile Phone Social Media Dependence Questionnaire scores （r=0.145， P<0.05）. Multiple linear regression analysis showed that compulsivity （β=0.416， P<0.01） and conflict （β=0.256， P<0.05） were significant predictors for adolescent NSSI behaviors.ConclusionAdolescent NSSI behaviors are associated with mobile phone social media dependence. The compulsivity and conflict dimension of mobile phone social media dependence are influencing factors for adolescent NSSI behaviors. The higher level of the compulsivity and conflict are associated with an increased risk of the NSSI behaviors in adolescents. ［Funde by Zhejiang Medical and Health Science and Technology Plan Project in 2022 （number， 2022KY704］
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".