The effect of financial support on depression among young adults during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background To mitigate the adverse effects of the COVID-19 pandemic on financial resources, governments provided financial support (e.g., emergency aid funds) as well as family via personal assistance. This study aims to assess the moderating effect of financial support from the government or from family on the association between income loss and depression among young adults. Methods Two online cross-sectional surveys among young adults (18-29) living in Canada and France were conducted in October-December 2020 (n = 4511) and July-December 2021 (n = 3329). Depressive symptoms were measured using PHQ-9 score+10. Two logistic regression models were performed for each survey with an interaction term between income loss and financial support (government or family modeled separately), controlling for demographics (e.g., country, age, gender, income, living conditions). Results In the total sample, half reported depressive symptoms (2020/2021: 53%/46%), and over a third lost income (2020/2021: 10%/12% all income, 38%/22% some income). In 2020, 41% received government financial support (2021: 18%) while family/friends support was constant (12%). In both surveys, among those who received government support, income loss was associated with depression, whether participants lost all income (2020: AOR 1.75 [1.29-2.44]; 2021: AOR 2.17 [1.36-3.44]), or some income (2020: AOR 1.31 [1.17-1.81]; 2021: AOR 1.46 [0.99-2.16]). However, among those who received family support, income loss was no longer significantly associated with depression, whether participants lost all income (2020: AOR 1.37 [0.78-2.40]; 2021: AOR 1.51 [0.88-2.56]), or some income (2020: AOR 1.31 [0.86-1.99]; 2021: AOR 1.10 [0.67-1.81]). Conclusions Association between income loss and depression was moderated by receipt of family financial support but not by receipt of government support. Financial support may help to mitigate the negative effects of income loss on young adults mental health during public health crisis. Key messages • Financial support may help to minimize risk of depressive symptoms among youth who lost income related to the COVID-19 pandemic. • Financial support through personal assistance (e.g., family, friends) appears to have a greater impact on youth mental health than COVID-specific government assistance funds.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".