Changes in household debt due to COVID-19 and mental health concerns among adults in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Canadian households experienced unexpected changes in their economic well-being during the COVID-19 pandemic. The extent of the impact of the pandemic on household debt and its effect on health and mental health remains unknown. AIM: The aim of the study was to examine the associations of change in household debt due to COVID-19 with serious psychological distress (SPD) and general health measures. METHODS: Data were from the 2020 Monitor study, a repeated cross-sectional survey of adults 18 years and older in Ontario, Canada. The 2020 cycle employed a web-based panel survey of 3,033 adults. The survey included measures of change in household debt due to the COVID-19 pandemic, mental and general health. Odds ratios (OR) were estimated from logistic regression models accounting for sociodemographic factors. RESULTS: Overall, 17.5% of respondents reported that their household debt increased due to the COVID-19 pandemic. Such an increase in household debt was significantly associated with SPD (OR = 2.92, 95% CI, 2.05-4.16), fair/poor mental health (OR = 2.02, 95% CI, 1.59-2.56), frequent mental distress days (OR = 1.80, 95% CI, 1.31-2.48), fair/poor general health (OR = 1.93, 95% CI, 1.47-2.52), and suicidal ideation (OR = 3.71, 95% CI, 2.41-5.70) after adjusting for potential confounders including education, income and employment. CONCLUSIONS: Household debt during the COVID-19 pandemic is an important determinant of health. Individuals who reported an increase in household debt due to COVID-19 were more likely to report serious mental health concerns including suicidal ideation. This suggests that debt-related interventions may be needed to alleviate the adverse effects of indebtedness on health.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".