Changes in self-reported cannabis use during the COVID-19 pandemic: a scoping review
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic is affecting mental health and substance use (MHSU) issues worldwide. The purpose of this study was to characterize the literature on changes in cannabis use during the pandemic and the factors associated with such changes. Methods We conducted a scoping review by searching peer-reviewed databases and grey literature from January 2020 to May 2022 using the Arksey and O’Malley Framework. Two independent reviewers screened a total of 4235 documents. We extracted data from 129 documents onto a data extraction form and collated results using content analytical techniques. Results Nearly half (48%) of the studies reported an increase/initiation of cannabis use, while 36% studies reported no change, and 16% reported a decrease/cessation of cannabis use during the pandemic. Factors associated with increased cannabis use included socio-demographic factors (e.g., younger age), health related factors (e.g., increased symptom burden), MHSU factors (e.g., anxiety, depression), pandemic-specific reactions (e.g., stress, boredom, social isolation), cannabis-related factors (e.g., dependence), and policy-related factors (e.g., legalization of medical/recreational cannabis). Conclusion Public health emergencies like the COVID-19 pandemic have the potential to significantly impact cannabis use. The pandemic has placed urgency on improving coping mechanisms and supports that help populations adapt to major and sudden life changes. To better prepare health care systems for future pandemics, wide-reaching education on how pandemic-related change impacts cannabis use is needed.
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 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 | 0.013 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".