Youth in a pandemic: a longitudinal examination of youth mental health and substance use concerns during COVID-19
Bibliographic record
Abstract
OBJECTIVE: This study analyses longitudinal data to understand how youth mental health and substance use are evolving over the course of the COVID-19 pandemic, which is critical to adjusting mental health response strategies. SETTING: Participants were recruited from among existing participants in studies conducted in an urban academic hospital in Ontario, Canada. PARTICIPANTS: A total of 619 youth aged 14-28 years participated in the study (62.7% girls/young women; 61.4% Caucasian). MEASURES: Data on mood, substance use and COVID-19-related worries were collected over four time points, that is, every 2 months beginning in the early stages of the pandemic in April 2020. Latent class analyses were conducted on the longitudinal data to identify distinct groups of youth who have different trajectory profiles of pandemic impact on their mood, substance use and COVID-19-related worries. RESULTS: For the majority of participants, mood concerns increased early in the pandemic, declined over Canada's summer months and subsequently increased in autumn. Among the youth with the highest level of mood symptoms at the beginning of the pandemic, increases in mental health concerns were sustained. Substance use remained relatively stable over the course of the pandemic. COVID-19-related worries, however, followed a trajectory similar to that of mood symptoms. Girls/young women, youth living in urban or suburban areas, in larger households, and with poorer baseline mental and physical health are the most vulnerable to mental health concerns and worries during the pandemic. CONCLUSIONS: Youth mental health symptom levels and concerns are evolving over the course of the COVID-19 pandemic, in line with the evolution of the pandemic itself, and longitudinal monitoring is therefore required. It is also essential that we engage directly with youth to cocreate pandemic response strategies and mental health service adaptations to best meet the needs of young people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".