Examining the influence of substance use on mental health rating during COVID-19: A Canadian perspective
Bibliographic record
Abstract
Introduction: Substance use and mental health symptoms (e.g., depression and anxiety) have increased during the COVID-19 pandemic, partly due to implementation of physical distancing measures aimed at containing the spread of the SARS-CoV-2 virus. However, there is limited pandemic-specific research that has examined the relationship between substance use and mental health with other correlates of well-being, including life satisfaction and social confidants. Methods: In the current study, we used ordered logistic regression analyses to examine whether a greater frequency of substance use (e.g., alcohol, cannabis, and opioids) during the pandemic predicted poorer ratings of self-reported mental health in a large sample of Canadians aged 15 to 64 years. We further considered whether life satisfaction and number of social confidants interacted with substance use to influence mental health, and stratified the models by sex and personal feelings of shame surrounding the use of substances (i.e., high and low shame). Results: Findings indicated that frequency of substance use was significantly associated with increased odds of reporting poorer mental health for males and females exhibiting both low and high shame. In females reporting low shame, we found that as frequency of cannabis use increased, life satisfaction has a much greater positive association with mental health. Whereas, in females disclosing high shame, maintaining social relations was particularly important to benefit the mental health of current users of opioids, relative to past and non-users. No such interaction was found in males. Discussion: Overall, the findings of the current study showed the negative mental health consequences of substance use during COVID-19 in a large Canadian sample, and most importantly revealed a critical sex difference in the way in which social determinants interact with substance use to influence mental health.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".