The Association Between Negative Online Behaviours and Problematic Social Media Use in Adolescents: Comparing Psychiatric Inpatients and Community Participants.
Bibliographic record
Abstract
Objectives: The U.S. Surgeon General's Advisory recently highlighted (2023) the urgency to better understand the impacts of social media on youth mental health, emphasizing the need to distinguish normative from problematic social media use (PSMU). This study aims to investigate specific online behaviours in adolescents, examining their association with PSMU, depressive symptoms and past abuse. Methods: 247 adolescents (ages 12-17) completed online questionnaires; 124 were part of the community group (CG) and 123 were psychiatric inpatients (Hospitalized Group, HG). The Bergen Social Media Addiction Scale (BSMAS) and PHQ-9 were used to evaluate PSMU and depressive symptoms. Additionally, participants reported on cyberbullying victimization, sexting, accessing self-harm content online and past abuse. Logistic regression analyses were used to measure the strength of association of different score predictors (BSMAS, PHQ-9, abuse) on negative behaviours. Results are presented as odds ratios (OR) with 95% confidence intervals (CI). Results: The prevalence rates of negative online behaviours were similar to those in the existing literature for cyber bullying victimization (20.5%), sexting (20.4%) and self-harm content seeking (27.6%). They were all more frequent in the hospitalized group. They were also associated with PSMU: cyber bullying victimization (OR 4.54, 95% CI [1.95-10.54]), sexting (OR 5.47, 95% CI [2.37-12.81]), and self-harm content seeking (OR 4.71, 95% CI [2.07-11.09]). Moreover, negative behaviours were associated with depressive symptoms as well as past physical and sexual abuse. Conclusions: Problematic social media use (PSMU) is associated with multiple negative online behaviours. Mental health professionals should include questions about social media use and online behaviours in their clinical assessment of adolescents.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".