Youth Vaping During the Early Stages of the COVID-19 Pandemic Period: Adjusted Annual Changes in Vaping Between the Pre-COVID and Initial COVID-Lockdown Waves of the COMPASS Study
Bibliographic record
Abstract
BACKGROUND: Adolescence is a critical period for vaping onset. The purpose of this article was to examine the effect of the early stages of the COVID-19 pandemic period on youth vaping. METHODS: We used 3-year linked data from the COMPASS study, including 7585 Canadian (Quebec, Ontario) adolescents from which 1949 completed all three survey waves (pre-COVID-19 [2018, 2019] and online [2020] during the early pandemic period [May-July 2020]) and provided vaping data. Structural equation modeling (SEM) and difference-in-difference (DD) models were used to estimate pre-COVID-19 to initial COVID-19 pandemic period change (2019-2020) in vaping (monthly, weekly, daily) compared with 2018-2019 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, the proportion of youth who were monthly and weekly vaping increased from 2018 to 2019 but decreased from 2019 to 2020; daily vaping increased across all waves. However, for all vaping outcomes modeled, 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. CONCLUSION: The early stages of the COVID-19 pandemic period appear to be associated with a reduction in the proportion of youth who were monthly and weekly vapers in our adjusted longitudinal models. While daily vaping increased over this same period of time, the magnitude of the increase in our adjusted longitudinal models appears attenuated by the early stages of the pandemic. IMPLICATIONS: This large prospective study of youth that included pre-pandemic data is unique in that we were able to identify that the early stages of the COVID-19 pandemic period was associated with a reduction in the proportion of youth who were monthly and weekly vapers in our adjusted longitudinal models. Conversely, the proportion of youth who were daily vaping increased over this same period of time, but the magnitude of the increase appears smaller than expected during the early stages of the pandemic in our adjusted longitudinal models. This study provides novel robust evidence that the patterns of vaping most aligned with onset and progression (i.e., monthly and weekly use) appear attenuated during the initial pandemic period.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".