Youth COVID-19 stressors and associations with self-perceived health, depression, anxiety, and at-risk alcohol and cannabis use
Bibliographic record
Abstract
Adolescents and young adults have been particularly vulnerable to disruptions caused by the COVID-19 pandemic. The objectives were to identify youth's self-reported pandemic-related stressors and examine how these stressors were related to six negative health outcomes: self-perceived, fair-to-poor physical, and mental health, depression, anxiety, and at-risk alcohol and cannabis use. Data were from the Well-Being and Experiences Study (The WE Study) from Manitoba, Canada (17–22 years old; n = 587; collected from November 2021 to January 2022). The COVID-19 stressors reported most frequently since pandemic onset included: (1) not being able to spend time with friends (78.5%); (2) feeling lonely or isolated (69.9%); and (3) remote learning for school, college, or university (68.4%). In reference to the “past month”, frequently reported stressors were (1) remote learning (42.6%); (2) feeling lonely or isolated (41.2%); and (3) uncertainty about the future (41.1%). Overall, 26.1% of the sample perceived their physical health as fair-to-poor and 59.1% perceive their mental health as fair-to-poor. A number of stressors were related to fair-to-poor mental health, depression, and anxiety; fewer were related to fair-to-poor physical health and at-risk alcohol and cannabis use. These findings can inform future pandemics and recovery efforts to improve pandemic-related health risks among youth.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".