The relationship between different aspects of social media use and mental health problems and life satisfaction among Norwegian students
Bibliographic record
Abstract
There are concerns about social media's potential impact on mental health and life satisfaction, but research results are mixed, focusing mainly on negative aspects and usage frequency. This study explores how different perceptions, actions, and motivations of social media use relate to Norwegian students' mental health and life satisfaction. The study included 47,163 full-time students aged 18–28 from the Norwegian Students' Health and Well-being Study (SHoT) in 2022. Mental health problems and life satisfaction were assessed through the Hopkins Symptoms Checklist (HSCL-25) and the Satisfaction With Life Scale (SWLS). Using Bayesian regression models, we investigated how ten statements about social media use were rated depending on mental health status, stratified by sex, and adjusted for age and parental education. Females reported significantly higher levels of mental health problems, lower life satisfaction, and higher agreement with most social media statements than males. However, the relationship between social media aspects and mental health was similar for both sexes, and age and parental education did not alter the results substantially. Notably, using social media as distraction from negative feelings was more prevalent among students with higher mental health problems and lower life satisfaction, whereas perceptions of positive attention on social media were lower. Some aspects, like fear of missing out, did not vary significantly across mental health status. These findings suggest a complex interplay between social media and mental health, with some behaviors potentially serving as forms of coping. This points to the importance of recognizing these complexities in future research and interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".