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Association of Internet Addiction With Nonsuicidal Self-injury Among Adolescents in China

2020· article· en· W3028636405 on OpenAlexaff
Jie Tang, Ying Ma, Stephen P. Lewis, Ruoling Chen, Angela Clifford, Brooke A. Ammerman, Marufu Martin Gazimbi, Adrian Byrne, Yu Wu, Xinchuan Lu, Hongjuan Chang, Chun Kang, Henning Tiemeier, Yizhen Yu

Bibliographic record

VenueJAMA Network Open · 2020
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Guelph
FundersNational Natural Science Foundation of ChinaEuropean Commission
KeywordsAddictionThe InternetPsychiatryMedicinePsychologyAssociation (psychology)Cross-sectional studyPublic healthInjury preventionPoison controlClinical psychologyEnvironmental healthNursing

Abstract

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Importance: Both nonsuicidal self-injury (NSSI), defined as the direct, deliberate damage of one's body tissue without suicidal intent, and internet addiction among adolescents are public health concerns. However, the possible association of NSSI with internet addiction is not well understood. Objective: To examine the occurrence of internet addiction with NSSI and any sex differences among Chinese adolescents. Design, Setting, and Participants: A multicenter, cross-sectional, survey study was conducted from February 18 to October 15, 2015, among adolescents aged 11 to 20 years from 343 classes in 45 public high schools across 5 provinces of China. Data analysis was performed from August 1, 2018, to March 1, 2019. Exposures: Possible internet addiction and internet addiction. Main Outcomes and Measures: Less-frequent (1-4 times) NSSI and more-frequent (≥5 times) NSSI were surveyed using the Chinese version of the Functional Assessment of Self-Mutilation. Results: A total of 15 623 students (8043 male [51.5%] and 7580 female [48.5%]) aged 11 to 20 years (mean [SD] age, 15.1 [1.8] years) participated. Of these, 4670 participants (29.9%) met the criteria for possible internet addiction and 509 participants (3.3%) met the criteria for internet addiction. A total of 2667 students (17.1%) engaged in less-frequent NSSI, while 1798 students (11.5%) engaged in more-frequent NSSI in the 12 months preceding the survey. Both possible internet addiction and internet addiction were associated with less-frequent or more-frequent NSSI. The adjusted odds ratios were 1.29 (95% CI, 1.17-1.42) for possible internet addiction and 1.41 (95% CI, 1.11-1.80) for internet addiction for less-frequent NSSI; for more-frequent NSSI, the adjusted odds ratios were 1.75 (95% CI, 1.56-1.96) for possible internet addiction and 2.66 (95% CI, 2.10-3.38) for internet addiction. These associations were similarly observed among age groups of 11 to 14, 15 to 17, and 18 to 20 years. No sex disparities were found in the associations of internet addiction with NSSI, except among adolescents aged 11 to 14 years, where the odds ratios for possible internet addiction with less-frequent NSSI were higher in male adolescents (1.53; 95% CI, 1.25-1.88) than female adolescents (1.13; 95% CI, 0.90-1.47). Conclusion and Relevance: Internet addiction appears to be associated with NSSI, and the findings of this study suggest that the association was similar between male adolescents and female adolescents. These data suggest that evaluation of the risk of NSSI for adolescents in association with internet addiction may help health care professionals in developing preventive interventions for NSSI.

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)0.001
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 score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.276
Teacher spread0.261 · 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".

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Citations138
Published2020
Admission routes1
Has abstractyes

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