Reexploring Problematic Social Media Use and Its Relationship with Adolescent Mental Health. Findings from the “LifeOnSoMe”-Study
Bibliographic record
Abstract
Purpose: Previous approaches used to assess problematic social media use risk inflating prevalence numbers and classifying unproblematic social media use as problematic. The main aim of this study was to take an exploratory view as to how different types of activities, experiences, and motivations on social media are associated with problematic mental health outcomes in adolescents. Patients and Methods: This study is based on a cross-sectional survey of 2023 adolescents (mean age 17.4 years (SD 0.9), 44.4% males) from the year 2020. Exploratory graph analysis and exploratory factor analysis were performed on 28 pre-selected items assessing adolescents' use of social media, to identify underlying potentially problematic factors associated with social media use. Sets of gender-adjusted multiple linear regression analyses were performed to assess the degree to which social media factors predicted depression, anxiety, well-being, and time spent on social media. Results: Three factors were identified: 1) "subjective overuse", 2) "social obligations", and 3) "source of concern". All three factors showed significant positive associations with mental health problems. The factor "source of concern", which identifies feelings of being overwhelmed and concerned over social media use, had the strongest association to mental health problems and simultaneously the weakest association to time spent on social media. Conclusion: Three identified factors measuring problematic social media use showed positive associations with mental health problems. This lends support to the notion that problematic social media use is a multidimensional phenomenon and demonstrates the need to move beyond addiction criteria when assessing problematic social media use.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".