In Sickness and in Wealth: Mental Health, Income Levels, and COVID-19
Bibliographic record
Abstract
Globally, COVID-19 has brought upon many challenges to mental health. Social distancing and isolation have led people to experience greater anxiety and negative affect, and financial distress has increased due to economic changes. Demographic features may differentiate the severity of distress individuals face. Using data from The Centre for Addiction and Mental Health (CAMH), the present study examined measures of psychological distress across a Canadian sample, identifying differences in age, sex, and income levels. Trends over time were observed. Lower-income Canadians reported higher distress. Women may be at greater risk than men, as well as younger compared to older Canadians. Psychological distress has remained relatively stable throughout the pandemic, although COVID-19 financial worry has lessened as people are not as worried about their finances. The findings of this study are informative of socioeconomic, sex, and age differences in mental health throughout the pandemic in a Canadian sample.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".