Investigating associations of social media use motives and mental well-being in adolescents
Bibliographic record
Abstract
Though most studies focus specifically on risks or potential negative impacts associated with social media use (Shannon et al., 2022), there is accumulating literature suggesting social media use can be either harmful or beneficial to adolescent mental well-being (e.g., Uhls et al., 2017). It has been suggested that the motives behind social media use might play a central role in determining its impact (Stewart, 2015). Therefore, we investigated whether adolescents’ motives for social media use are associated with their mental health symptoms. We recruited an online sample of 1740 adolescents not currently receiving mental health treatment and analyzed their baseline questionnaire data from an ongoing longitudinal study. We found that negative reinforcement motives for social media use (coping and conformity) were associated with higher internalizing ( B = 0.32 and B = 0.22, respectively) and externalizing symptoms ( B = 0.20; B = 0.16) in adolescent social media users, whereas positive reinforcement motives (social and enhancement) were associated with lower internalizing ( B = −0.25; B = −0.11) and externalizing symptoms ( B = −0.16; B = −0.18). Social motives were also associated with greater self-reported pro-sociality ( B = 0.10). The harmful or beneficial effects of social media on adolescent mental well-being may, thus, depend on the motives for its use. Interventions may benefit from targeting motives for social media use, particularly when social media use behaviors are driven by high negative reinforcement motives.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".