Self-Isolation: A Significant Contributor to Cannabis Use during the Covid-19 Pandemic
Bibliographic record
Abstract
Background Emerging research suggests the COVID-19 pandemic has resulted in a significant increase in self-reported isolation and loneliness in a large proportion of the population. This is particularly concerning given that isolation and loneliness are associated with increased cannabis use, as well as using cannabis to cope with negative affect. Objective : We investigated whether self-isolation due to COVID-19 and using cannabis to cope with depression were unique and/or interactive predictors of cannabis use during the pandemic, after controlling for pre-pandemic levels of cannabis use. Method A sample of 70 emerging adults (mean age = 23.03; 34.3% male) who used both alcohol and cannabis pre-pandemic completed measures of cannabis use (i.e., quantity x frequency) and a novel COVID-19 questionnaire between March 23 and June 15, 2020. Pre-pandemic cannabis use levels had been collected four months earlier. Results Linear regressions indicated self-isolation and coping with depression motives for cannabis use during the pandemic were significant predictors of pandemic cannabis use levels after accounting for pre-pandemic use levels. There was no interaction between coping with depression motives and self-isolation on cannabis use during the pandemic. Conclusions Those who engaged in self-isolation were found to use 20% more cannabis during the pandemic than those who did not. Our results suggest that self-isolation is a unique risk factor for escalating cannabis use levels during the pandemic. Thus, self-isolation may inadvertently lead to adverse public health consequences in the form of increased cannabis use.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".