Mental health trajectories among the general population and higher-risk groups following the COVID-19 pandemic in Switzerland, 2021–2023
Bibliographic record
Abstract
BACKGROUND: Mental health deteriorated in the early stages of the COVID-19 pandemic, but improved relatively quickly as restrictions were eased, suggesting overall resilience. However, longer-term follow-up of mental health in the general population is scarce. METHODS: We examined mental health trajectories in 5624 adults (58 % women; aged 18-97 years) from the Specchio-COVID19 cohort, using the Generalized Anxiety Disorder scale-2 and the Patient Health Questionnaire-2, administered each month from February to June 2021, and in Spring 2022 and 2023. RESULTS: Depressive and anxiety symptoms declined during a pandemic wave from February to May 2021 (β = -0.06 [-0.07, -0.06]; -0.06 [-0.07, -0.05]), and remained lower at longer-term follow-up than at the start of the wave. Loneliness also declined over time, with the greatest decline during the pandemic wave (β = -0.25 [-0.26, -0.24]). Many higher-risk groups, including socioeconomically disadvantaged individuals, those with a chronic condition, and those living alone had poorer mental health levels throughout the study period. Women and younger individuals had a faster improvement in mental health during the pandemic wave. Loneliness trajectories were associated with mental health trajectories throughout the study period. LIMITATIONS: We cannot definitively conclude that the observed changes in mental health were due to experiences of the pandemic. CONCLUSIONS: While there was a need for additional mental health support during stricter policy responses to COVID-19, overall, mental health improved relatively soon after measures were eased. Nevertheless, the persistence of mental health disparities highlights the need for further efforts from the government and healthcare practitioners to support vulnerable groups beyond the pandemic.
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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 | 0.000 | 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".