Covid-19 Stress, Smoking and Heavy Drinking Behaviors in University Students in Singapore
Bibliographic record
Abstract
Aims: This study investigates multi-dimensional Covid-19 related stressors and the extent to which these stressors are related to young adult’s smoking and heavy alcohol use among university students in Singapore during the Covid-19 pandemic. Design/Setting/Participants: A total of 2,339 undergraduate students (862 male, 1309 female) in Singapore participated in an online survey. Measures: The Covid-19 Stressors Questionnaire (C19SQ) is a developmentally influenced, Covid-19 related stress scale, used alongside mental health measures (GAD & PHQ) and substance use. Structural equation modeling was used to model the association between four types of Covid-19 related stressors and past month smoking and heavy drinking while accounting for student mental health, family socio-economic status, gender and race/ethnicity. Findings: Stress related to Covid-19 social restrictions was associated with increased likelihood of heavy drinking and smoking behavior, whereas stress related to health concerns was associated with decreased likelihood of smoking and heavy drinking. Covid-19 related stressors related to resource constraints and future uncertainties were not associated with young adult substance use. Conclusions: Our finding that Covid-19 stressors related to Social Restrictions and Health Concerns were particularly salient for university student’s substance use, but in opposite directions, extend prior work by demonstrating that young adult substance use behavior is differentially impacted by specific types of Covid-19 stress and point to areas where public health and clinical intervention efforts can focus in the wake of the Covid-19 pandemic for this vulnerable population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".