Instagram Participation and Substance Use Among Emerging Adults: The Potential Perils of Peer Belonging
Bibliographic record
Abstract
Emerging adults (ages 18–29) have the highest rates of both harmful drinking and participation on social network sites (SNSs) compared to adolescents and older adults. In fact, greater SNS participation has been shown to predict greater alcohol use. Little is known, however, about noncollege samples, substances apart from alcohol, and SNSs other than Facebook. Furthermore, few studies have examined what might moderate any observed influence of SNS participation on substance use. In this study, we used hierarchical linear and negative binomial regression analyses to examine the unique associations between Instagram participation and alcohol as well as marijuana use, controlling statistically for demographic characteristics, peer norms, and social status, in a community sample of emerging adults (N = 194). We also tested whether peer belonging or motives for Instagram participation moderated these relationships. Results showed that Instagram participation was positively related to alcohol use only for those with high levels of peer belonging. The initial negative association between Instagram participation and marijuana use disappeared once peer norms and social status were included. Peer norms were positively related to both alcohol and marijuana use, while peer belonging was positively related to marijuana use. Peer belonging appears to be an important variable in the study of SNSs and substance use among emerging adults. Future work might test the somewhat counterintuitive hypotheses raised by these findings that peer belonging sensitizes individuals to SNS influences on drinking and could be a marker of greater marijuana 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".