Psychological Resilience and Adverse Mental Health Issues in the Thai Population during the Coronavirus Disease 2019 Pandemic
Bibliographic record
Abstract
In light of the coronavirus disease 2019 (COVID-19) pandemic and the enormous amount of uncertainty caused by it, mental health issues have become a great concern. Evidence regarding the effects of psychological resilience on the Thai population is scarce. We evaluated psychological resilience during the first wave of the COVID-19 pandemic and its association with the risk of mental health outcomes, such as depression, anxiety, stress, and health-related well-being. This cross-sectional study was a part of the HOME-COVID-19 project, which conducted an online survey of 4004 members of the general population in Thailand using the Brief Resilience Coping Scale. Logistic regression was performed to identify the association between psychological resilience and mental health issues and well-being. Groups with prevalence rates of 43.9%, 39.2%, and 16.9% were classified as low, moderate, and high resilient copers, respectively. Using high resilient copers as a reference group, the low resilient copers had a higher chance of having mental health adversities. The adjusted odds ratio (OR) was 1.89 (95% confidence interval [CI], 1.39–2.56; p < 0.001) for depression, 2.13 (95% CI, 1.45–3.14; p < 0.001) for anxiety, 4.61 (95% CI, 3.30–6.45; p < 0.001) for perceived stress, and 3.18 (95% CI, 2.31–4.38; p < 0.001) for low well-being. For the medium resilient copers, only low well-being was found to be statistically significant (OR, 1.60; 95% CI, 1.16–2.20; p = 0.004). It is important that resilience be considered in the development of strategies for managing the COVID-19 pandemic to prevent or reduce adverse mental health outcomes.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".