Mental Health of People in State Quarantine during COVID-19 Situation in Thailand
Bibliographic record
Abstract
When the COVID-19 outbreak spread across the globe, Thailand was the first country to report a COVID patient outside of China. We conducted a cross-sectional descriptive study to examine the mental health condition and the risk factors associated with the mental health problems of people in state quarantine. Our study sample included 4,069 people who were in state quarantine in the eastern region of Thailand. We administered a stress assessment test, a depression screening questionnaire, a suicidal risks screening tool and a COVID-19 anxiety screening scale, which were developed by the Department of Mental Health, Thailand. We found that most people in state quarantine reported a moderate level of COVID-19 anxiety, a mild level of stress, and no current risk of suicide. The risk factors associated with stress were female gender (OR = 2.290, p < 0.001, 95% CI [1.687, 3.109]) and having chronic diseases (OR = 2.443, p < 0.001, 95%CI [1.720, 3.470]). The factor associated with depression was female gender (OR = 1.380, p < 0.001, 95%CI [1.201, 1.586]). The factors associated with risks for suicide were female gender (OR = 2.059, p < 0.001, 95%CI [1.553, 2.729]) and having chronic diseases (OR = 2.128, p < 0.001, 95%CI [1.510, 2.998]). The factors associated with COVID-19 anxiety were female gender (OR = 1.469, p < 0.001, 95%CI [1.294, 1.669]) and having chronic diseases (OR = 1.329, p = 0.011, 95%CI [1.066, 1.657]). A system to screen for mental health problems and rapid assistance offered to people in state quarantine who are at risk of mental health problems are recommended to reduce the severity of the problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".