A Longitudinal Cohort Study of Youth Mental Health and Substance use Before and During the COVID-19 Pandemic in Ontario, Canada: An Exploratory Analysis
Bibliographic record
Abstract
BACKGROUND: Youth mental health appears to have been negatively impacted by the COVID-19 pandemic. The impact on substance use is less clear, as is the impact on subgroups of youth, including those with pre-existing mental health or substance use challenges. OBJECTIVE: This hypothesis-generating study examines the longitudinal evolution of youth mental health and substance use from before the COVID-19 pandemic to over one year into the pandemic among youth with pre-existing mental health or substance use challenges. METHOD: A total of 168 youth aged 14-24 participated. Participants provided sociodemographic data, as well as internalizing disorder, externalizing disorder, and substance use data prior to the pandemic's onset, then every two months between April 2020-2021. Linear mixed models and Generalized Estimating Equations were used to analyze the effect of time on mental health and substance use. Exploratory analyses were conducted to examine interactions with sociodemographic and clinical characteristics. RESULTS: There was no change in internalizing or externalizing disorder scores from prior to the pandemic to any point throughout the first year of the pandemic. Substance use scores during the pandemic declined compared to pre-pandemic scores. Exploratory analyses suggest that students appear to have experienced more mental health repercussions than non-students; other sociodemographic and clinical characteristics did not appear to be associated with mental health or substance use trajectories. CONCLUSIONS: While mental health remained stable and substance use declined from before the COVID-19 pandemic to during the pandemic among youth with pre-existing mental health challenges, some youth experienced greater challenges than others. Longitudinal monitoring among various population subgroups is crucial to identifying higher risk populations. This information is needed to provide empirical evidence to inform future research directions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".