Peer Connections and Social Media Use Among Military-Connected Youth in Canada: A Cross-Sectional Study
Bibliographic record
Abstract
Introduction: Peer relationships and social media use play a crucial role in the social and emotional development of adolescents. Military-connected-youth in Canada may experience unique stressors resulting from having a parent(s) or guardian(s) in the Canadian Armed Forces that could impact their peer relationships and use of social media. The objectives of this study were to describe peer connections and explore use of online communication and social media among Canadian youth in military-connected families, and compare the likelihood of low peer support or problematic social media use to those not in military-connected families. Methods: This was a cross-sectional study using 2017/18 Health Behaviour in School-aged Children in Canada survey data which includes a large sample of youth attending Grades 6 to 10. Questionnaires collected information on parental/guardian current or previous service in the Canadian Armed Forces, peer support, use of online communication, presence of problematic social media use, and sociodemographic variables. Multivariable log binomial regression models adjusting for sex, grade, geography, and family affluence were used to estimate prevalence risk ratios, applying survey weights and accounting for clustering by school. Effect modification by sex and grade were investigated. Results: Youth with a parent or guardian who currently or previously served in the Canadian Armed Forces had a higher prevalence of low peer support compared to their non-military- connected peers (20.63% N=328, compared to 15.8% N= 2605). After adjusting for sex, grade, geography, and family affluence, military-connected youth were 28% more likely to report low peer support compared to their non-military-connected peers (RR 1.28, 95% CI: 1.09-1.47, p-value= 0.001). We did not detect an association between having a military-connected parent/guardian and problematic social media use (RR 1.11, 95% CI: 0.84-1.47, p-value= 0.46). There is no statistically significant evidence that grade-group or sex changes the relationship between military family membership and our outcomes. Conclusion: There is a need for targeted efforts to support peer connections among military- connected youth in Canada. Further research to determine the factors contributing to this difference in peer support and understand potential benefits of social media use among this population are needed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".