Mental Health of the General Population during the 2019 Coronavirus Disease (COVID-19) Pandemic: A Tale of Two Developing Countries
Bibliographic record
Abstract
Background: This study aimed to compare the severity of psychological impact, anxiety and depression between people from two developing countries, Iran and China, and to correlate mental health parameters with variables relating to the COVID-19 pandemic. Although China and Iran are developing countries based on the World Bank’s criteria, these two countries are different in access to resources and health care systems. We hypothesized that Iranians would show higher levels of depression, anxiety and stress as compared to Chinese. Methods: This study collected information related to the COVID-19 pandemic including physical health, precautionary measures and knowledge about the pandemic. We also used validated questionnaires such as the Impact of Event Scale-Revised (IES-R) and the Depression, Anxiety and Stress Scale (DASS-21) to assess the mental health status. Results: There were a total of 1411 respondents (550 from Iran; 861 from China). The mean IES-R scores of respondents from both countries were above the cut-off for post-traumatic stress disorder (PTSD) symptoms. Iranians had significantly higher levels of anxiety and depression (p < 0.01). Significantly more Iranians believed COVID-19 was transmitted via contact, practised hand hygiene, were unsatisfied with health information and expressed less confidence in their doctors, but were less likely to wear a facemask (p < 0.001). Significantly more Iranians received health information related to COVID-19 via television while Chinese preferred the Internet (p < 0.001). Conclusions: This cross-country study found that Iranians had significantly higher levels of anxiety and depression as compared to Chinese. The difference in reported measures between respondents from Iran and China were due to differences in access to healthcare services and governments’ responses to the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".