Examining the impact of the early stages of the COVID-19 pandemic period on youth cannabis use: adjusted annual changes between the pre-COVID and initial COVID-lockdown waves of the COMPASS study
Bibliographic record
Abstract
BACKGROUND: Given the high rates of cannabis use among Canadian youth and that adolescence is a critical period for cannabis use trajectories, the purpose of this paper was to examine the effect of the early stages of the COVID-19 pandemic period on youth cannabis use in the context of a natural experiment. We used 3-year linked data from the COMPASS study, including 7653 Canadian (Quebec, Ontario) adolescents from which 1937 completed all 3 survey waves (pre-COVID-19 [2018, 2019] and online [2020] during the early pandemic period [May-July 2020]). Structural equation modeling (SEM) and double difference (DD) models were used to estimate pre-COVID-19 to initial COVID-19 pandemic period change (2019-2020) in cannabis use (monthly, weekly, daily) compared to 2018 to 2019 change to adjust for age-related effects. Models were adjusted for age of entry into the cohort and sociodemographic characteristics. RESULTS: In the SEM and DD models, monthly, weekly, and daily cannabis use increased across all waves; however, the expected increases from the pre-COVID-19 wave (2019) to the initial COVID-19 period wave (2020) were lesser relative to the changes seen across the 2018 to 2019 waves. The cross-sectional data from May to July 2020 identified that the majority of youth who use cannabis did not report increased cannabis use due to COVID-19 or using cannabis to cope with COVID-19. CONCLUSION: During the early stages of the COVID-19 pandemic period, there does not appear to be a detrimental effect on youth cannabis use, when adjusted for age-related changes. Further prospective research is needed to explore the impact of the ongoing pandemic response on youth cannabis use onset and progression.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".