Demographics Associated With Stress, Severe Mental Distress, and Anxiety Symptoms During the COVID-19 Pandemic in Japan: Nationwide Cross-sectional Web-Based Survey
Bibliographic record
Abstract
BACKGROUND: With the spread of COVID-19, the deterioration of public mental health has become a major global and social problem. OBJECTIVE: The purpose of this study was to elucidate the relationship between the 3 mental health problems associated with COVID-19, that is, perceived stress, severe mental distress, and anxiety symptoms, and the various demographic factors, including occupation. METHODS: A nationwide web-based questionnaire survey was conducted in Japan from August 4 to 31, 2020. In addition to sociodemographic data, the degrees of perceived stress, severe mental distress, and anxiety symptoms associated with COVID-19 were measured. After performing a descriptive statistical analysis, factors related to stress, severe mental distress, and anxiety symptoms were analyzed using logistic regression analysis. RESULTS: A total of 8203 respondents submitted survey responses, among whom 34.9% (2861/8203) felt intense stress associated with COVID-19, 17.1% (1403/8203) were depressed, and 13.5% (1110/8203) had severe anxiety symptoms. The logistic regression analysis showed that each of the 3 mental health problems were prevalent in females, nonbinary gender, people in their 50s, 60s and older, respondents who visited psychiatrists, and those currently in psychiatric care. Severe mental distress and anxiety symptoms were associated with the number of effective lifestyle coping strategies during the lockdown period. Severe mental distress was only prevalent in teenagers and respondents in their 20s, as students tended to develop stress and severe mental distress. With regard to occupation, working in nursing care and welfare, education and research, and medical and health sectors was associated with stress; however, working in these occupations was not associated with severe mental distress and anxiety symptoms. Unemployment was associated with severe mental distress and anxiety symptoms. All 3 mental health problems were prevalent in part-time workers and those working in entertainment and arts sectors. CONCLUSIONS: Gender, age, occupation, history of psychiatric visits, and stress coping mechanisms were associated with mental health during the COVID-19 pandemic, but their associations with stress, severe mental distress, and anxiety symptoms differed. In addition, the actual state of mental health varied according to the respondents' occupation. It is necessary to consider the impact of the COVID-19 pandemic on mental health not only at the individual level but also at the occupational level.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".