Problematic Social Media Use and Mental Health in Canadian Adolescents
Bibliographic record
Abstract
Background: Adolescent mental health problems have become increasingly common in recent years. Problematic social media use (PSMU) has recently gained attention as a possible risk factor for mental health problems. However, the relationship between problematic social media use and adolescent mental health problems remains understudied, and few studies have specifically examined mediated pathways linking the two phenomena. Foundational knowledge about such pathways is necessary to support the development of clinical interventions to reduce the psychological burden of problematic social media use. Objectives: The objectives of this thesis are to: 1) estimate the relative risk for reporting two or more indicators of internalizing mental health problems from problematic social media use, 2) estimate the extent to which exposure to cyberbullying victimization mediates the relationship between problematic social media use and indicators of internalizing mental health problems in adolescents, and 3) to investigate whether gender modifies either the direct or mediated effect of problematic social media use on indicators of internalizing mental health problems. Methods: Data from Cycle 8 of the Canadian Health Behaviour in School-aged Children study was used. Direct and mediated effects were estimated using counterfactual mediation analysis. Effects were estimated for the entire sample and for each gender. Results: Participants who engaged in problematic social media use were 1.59 (95% CI: 1.47 – 1.73) times as likely to report multiple psychological health complaints. Cyberbullying victimization did not mediate the total effect of PSMU on multiple psychological health complaints in isolation. Results were consistent between genders. Conclusions: PSMU is an important risk factor for multiple psychological health complaints in Canadian adolescents. Cyberbullying victimization does not appear to mediate the relationship between problematic social media use and multiple psychological health complaints in isolation, nor does this differ between boys and girls.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".