Changes in socioeconomic resources and mental health after the second COVID-19 wave (2020–2021): a longitudinal study in Switzerland
Bibliographic record
Abstract
BACKGROUND: During the 2020/2021 winter, the labour market was under the impact of the COVID-19 pandemic. Changes in socioeconomic resources during this period could have influenced individual mental health. This association may have been mitigated or exacerbated by subjective risk perceptions, such as perceived risk of getting infected with SARS-CoV-2 or perception of the national economic situation. Therefore, we aimed to determine if changes in financial resources and employment situation during and after the second COVID-19 wave were prospectively associated with depression, anxiety and stress, and whether perceptions of the national economic situation and of the risk of getting infected modified this association. METHODS: One thousand seven hundred fifty nine participants from a nation-wide population-based eCohort in Switzerland were followed between November 2020 and September 2021. Financial resources and employment status were assessed twice (Nov2020-Mar2021, May-Jul 2021). Mental health was assessed after the second measurement of financial resources and employment status, using the Depression, Anxiety and Stress Scale (DASS-21). We modelled DASS-21 scores with linear regression, adjusting for demographics, health status, social relationships and changes in workload, and tested interactions with subjective risk perceptions. RESULTS: We observed scores above thresholds for normal levels for 16% (95%CI = 15-18) of participants for depression, 8% (95%CI = 7-10) for anxiety, and 10% (95%CI = 9-12) for stress. Compared to continuously comfortable or sufficient financial resources, continuously precarious or insufficient resources were associated with worse scores for all outcomes. Increased financial resources were associated with higher anxiety. In the working-age group, shifting from full to part-time employment was associated with higher stress and anxiety. Perceiving the Swiss economic situation as worrisome was associated with higher anxiety in participants who lost financial resources or had continuously precarious or insufficient resources. CONCLUSION: This study confirms the association of economic stressors and mental health during the COVID-19 pandemic and highlights the exacerbating role of subjective risk perception on this association.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".