A cross-sectional study of mental health and well-being among youth in military-connected families
Bibliographic record
Abstract
INTRODUCTION: The study objective was to compare the mental health and risk-taking behaviour of Canadian youth in military-connected families to those not in military-connected families in a contemporary sample. We hypothesized that youth in military-connected families have worse mental health, lower life satisfaction and greater engagement in risk-taking behaviours than those not in military-connected families. METHODS: This cross-sectional study used 2017/18 Health Behaviour in School-aged Children in Canada survey data, a representative sample of youth attending Grades 6 to 10. Questionnaires collected information on parental service and six indicators of mental health, life satisfaction and risk-taking behaviour. Multivariable Poisson regression models with robust error variance were implemented, applying survey weights and accounting for clustering by school. RESULTS: This sample included 16 737 students; 9.5% reported that a parent and/or guardian served in the Canadian military. After adjusting for grade, sex and family affluence, youth with a family connection to the military were 28% more likely to report low well-being (95% CI: 1.17-1.40), 32% more likely to report persistent feelings of hopelessness (1.22-1.43), 22% more likely to report emotional problems (1.13-1.32), 42% more likely to report low life satisfaction (1.27-1.59) and 37% more likely to report frequent engagement in overt risk-taking (1.21-1.55). CONCLUSION: Youth in military-connected families reported worse mental health and more risk-taking behaviours than youth not in military-connected families. The results suggest a need for additional mental health and well-being supports for youth in Canadian military-connected families and longitudinal research to understand underlying determinants that contribute to these differences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".