Substance-related coping behaviours among youth during the early months of the COVID-19 pandemic
Bibliographic record
Abstract
As impacts of the COVID-19 pandemic continue to unfold, research is needed to understand how school-aged youth are coping with COVID-19-related changes and disruptions to daily life. Among a sample of Canadian youth, our objective was to examine the mental health factors associated with using substances to cope with COVID-19-related changes, taking account of expected sex differences. We used online data collected from 7150 students in the COMPASS study, during the early months of the COVID-19 pandemic (May–July 2020) in British Columbia, Ontario, and Quebec, Canada. We specified a sex-stratified, generalized linear mixed model to estimate the likelihood of engagement in substance-related coping behaviours, while testing for the effects of students’ mental health, individual characteristics, and school neighborhood characteristics. Twelve percent of students (13.4% of females, 9.9% of males) in our sample reported using cannabis, alcohol, cigarettes, and/or vaping to help cope with COVID-19-related changes. Regardless of sex, students with greater depressive symptoms were more likely to engage in substance-related coping (aORFemale = 1.04, 95 %CI[1.01–1.07]; aORMale = 1.06, 95 %CI[1.013–1.11]). Among females, better psychosocial wellbeing was protective against engagement in substance related-coping (aOR = 0.96, 95 %CI[0.94–0.98]), controlling for current substance use. Canadian school-aged youth with generally poor mental health may be more likely to have engaged in substance use to help cope with COVID-19-related changes during the first wave of the pandemic, and female youth may be at disproportionate risk of engaging in the behaviour. Ongoing evaluation of the impacts of COVID-19 on youth health is required.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".