Moderation of the association between COVID-19-related income loss and depression by receipt of financial support: Repeated cross-sectional surveys of young adults in Canada and France (2020–2021)
Bibliographic record
Abstract
Background: To mitigate the adverse effects of the COVID-19 pandemic on financial resources, governments and family/friends mobilized financial support interventions (e.g., emergency aid funds) and assistance. However, little is known about how financial assistance alleviated mental health problems. This study aimed to investigate the moderating effect of financial support from the government or from family/friends on the association between income loss and depression among young adults. Methods: ≥10). Two logistic regression models were performed for each survey with an interaction term between income loss and financial support (government or family/friends modeled separately), controlling for demographics. Results: Overall, half reported depressive symptoms (2020/2021: 53.5%/45.6%), and over a third lost income (2020/2021: 10.2%/11.6% all income, 37.7%/21.6% some income). In 2020, 40.6% received government financial support (17.7% in 2021) while family/friends support was received by 12% (in both surveys). In both surveys, among those who received governmental financial support, income loss was associated with depression, whether participants lost all their income (e.g., 2020: Adjusted Odds Ratios (AOR) 1.75, 95% Confidence Interval [1.29-2.44]), or some of their income (e.g., 2020: AOR 1.45 [1.17-1.81]). However, among those who received family/friends financial support, income loss was no longer significantly associated with depression in both cycles, whether participants lost all their income (e.g., 2020: AOR 1.37 [0.78-2.40]), or some of their income (e.g., 2020: AOR 1.31 [0.86-1.99]). Conclusions: Association between income loss and depression was moderated by receipt of family/friends financial support but not by receipt of government financial support. Financial support interventions may help to mitigate the negative effects of income loss on young adults mental health during periods of economic crisis.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".