An Impact Analysis of the Early Months of the COVID-19 Pandemic on Mental Health in a Prospective Cohort of Canadian Adolescents
Bibliographic record
Abstract
PURPOSE: The impact of the COVID-19 pandemic on adolescent mental health is a global concern; however, most research is cross-sectional or started after the pandemic response began and thus unable to evaluate within-individual change. The purpose of this prospective study was to evaluate the effect of the initial COVID-19 response on adolescent mental health and ill-health as a natural experiment. METHODS: We used 3-year linked data from the COMPASS study, including 7,653 Canadian (Quebec, Ontario) adolescents from which 2,099 completed surveys in all three waves (pre-COVID-19 [2018 and 2019] and online [May-July 2020], 2-3 months into the pandemic). A structural equation modeling approach to fixed effects and a difference-in-differences design were used to estimate pre-COVID-19-to-early lockdown change in mental health (psychosocial well-being [flourishing-reverse scored]) and ill-health (depression and anxiety symptoms), compared with 2018-to-2019 change. Models were adjusted for self-selection, age of entry into the cohort, and sociodemographics. RESULTS: Depression, anxiety, and reverse-flourishing scores increased across all waves; however, the mental health changes from the pre-COVID-19 wave (2019) to 2020 were not greater relative to the changes seen across the 2018-to-2019 waves. CONCLUSIONS: Our results do not support a detrimental effect of the initial stages of the COVID-19 lockdown measures on adolescent mental health. The deterioration in mental health in the early COVID-19 response was less than the decline found over a prepandemic period. Further prospective research is needed to explore the impact of the prolonged pandemic and related measures on adolescents and inequitable effects in population subgroups.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".