Health, Psychological Distress, and Functioning During the COVID-19 Pandemic Among Danish Adults with and Without a Preexisting Mental Illness
Bibliographic record
Abstract
The aim of this paper was to evaluate health, psychological distress, and functioning during the COVID-19 pandemic among Danish adults with and without a history of mental illness. Data were drawn from three online surveys conducted in May 2020 (n = 3134), January 2021 (n = 1170), and January 2022 (n = 1174) as part of the Danish contribution to the Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT). The prevalence of mental and physical health issues, psychological distress (stress, sleep problems, loneliness, and boredom) and levels of functioning (self-care, interpersonal relationships, hobbies/leisure, and work/education) were evaluated at four different time points stratified by history of mental illness. Findings indicated that physical health was not differentially affected between people with and without prior mental illness. However, mental health declined significantly more among respondents with a history of mental illness. While levels of stress did not differ between the two groups, boredom was more pronounced in May 2020 among those with prior mental illness. Loneliness was significantly higher in this group in January 2021. Sleep disturbances were more pronounced for respondents with former mental illness during the whole period. A decline in functioning was observed in people both with and without a former mental illness. It seemed a little more pronounced for people with mental illness but seldom reached statistical significance. For all measures of health, distress, and functioning, 10-20% of respondents reported improvements in health, distress, and functioning during the pandemic, with stress showing the most improvement-one third of participants reported feeling less stressed. In most of the parameters measured, the influence of the COVID-19 pandemic seemed to decrease with time. However, the effects were not uniform, and more investigations are needed to understand the whole picture.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".