Patterns of multiple health risk–behaviours in university students and their association with mental health: application of latent class analysis
Bibliographic record
Abstract
INTRODUCTION: University and college campuses may be the last setting where it is possible to comprehensively address the health of a large proportion of the young adult population. It is important that health promoters understand the collective challenges students are facing, and to better understand the broader lifestyle behavioural patterning evident during this life stage. The purpose of this study was to examine the clustering of modifiable health-risk behaviours and to explore the relationship between these identified clusters and mental health outcomes among a large Canadian university sample. METHODS: Undergraduate students (n = 837; mean age = 21 years) from the University of Toronto completed the National College Health Assessment survey. The survey consists of approximately 300 items, including assessments of student health status, mental health and health-risk behaviours. Latent class analysis was used to identify patterning based on eight salient health-risk behaviours (marijuana use, other illicit drug use, risky sex, smoking, binge drinking, poor diet, physical inactivity, and insufficient sleep). RESULTS: A three-class model based on student behavioural patterns emerged: "typical," "high-risk" and "moderately healthy." Results also found high-risk students reporting significantly higher levels of stress than typical students (χ2(1671) = 7.26, p < .01). CONCLUSION: Students with the highest likelihood of engaging in multiple health-risk behaviours reported poorer mental health, particularly as it relates to stress. Although these findings should be interpreted with caution due to the 28% response rate, they do suggest that interventions targeting specific student groups with similar patterning of multiple health-risk behaviours may be 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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".