Associations between social media use and positive mental health among adolescents: Findings from the Canadian Health Behaviour in School-aged Children Study
Bibliographic record
Abstract
Social media use (SMU) has rapidly increased among children and adolescents in the past decade, and has been linked with poorer mental health. However, moderate SMU can facilitate connectedness among peers, which may have positive impacts for well-being - these associations are underexplored. This study examined the associations between SMU and several indicators of PMH in a nationally representative sample of 13,113 adolescents in grades 6 to 10 from the 2017/2018 cycle of the Canadian Health Behavior in School-aged Children (HBSC) study. SMU was measured using a four-level composite of intensity (non-active, active, intense) and problematic SMU (addictive-like symptoms, regardless of intensity). Four dichotomous indicators of PMH were examined: self-efficacy, self-confidence, life satisfaction, and psychological well-being. Covariates included grade, gender, cultural/racial background, and socioeconomic status. Unadjusted and adjusted mixed effects logistic regression models accounting for school-level clustering were run, with separate models for each PMH indicator and active SMU set as the referent. When compared to active SMU, problematic SMU was associated with lower levels of all PMH indicators, and intense SMU was associated with lower life satisfaction. In gender-stratified models, intense SMU was associated with lower life satisfaction among girls. In grade-stratified models, non-active SMU was associated with lower self-confidence for adolescents in grades 9 and 10. In all, problematic SMU is generally associated with lower odds of reporting high PMH. Longitudinal research is required to ascertain directionality of these findings, and to examine how these associations may have changed as a result of the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".