COVID‐19 impacts on drinking and mental health in emerging adults: Longitudinal changes and moderation by economic disruption and sex
Bibliographic record
Abstract
BACKGROUND: There are significant concerns that the COVID-19 pandemic may have negative effects on substance use and mental health, but most studies to date are cross-sectional. In a sample of emerging adults, over a two-week period during the pandemic, the current study examined: (1) changes in drinking-related outcomes, depression, anxiety, and posttraumatic stress disorder and (2) differences in changes by sex and income loss. The intra-pandemic measures were compared to pre-pandemic measures. METHODS: = 23.84; 41.7% male) in an existing longitudinal study on alcohol misuse who were assessed from June 17 to July 1, 2020, during acute public health restrictions in Ontario, Canada. These intra-pandemic data were matched to participant pre-pandemic reports, collected an average of 5 months earlier. Assessments included validated measures of drinking, alcohol-related consequences, and mental health indicators. RESULTS: Longitudinal analyses revealed significant decreases in heavy drinking and adverse alcohol consequences, with no moderation by sex or income loss, but with substantial heterogeneity in changes. Significant increases in continuous measures of depression and anxiety were present, both of which were moderated by sex. Females reported significantly larger increases in depression and anxiety. Income loss >50% was significantly associated with increases in depression. CONCLUSIONS: During the initial phase of the pandemic, reductions in heavy drinking and alcohol consequences were present in this sample of emerging adults, perhaps due to restrictions on socializing. In contrast, there was an increase in internalizing symptoms , especially in females, highlighting disparities in the mental health impacts of the pandemic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".