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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".