Public concerns during the COVID-19 lockdown – a multicultural cross-sectional study in three countries (Preprint)
Bibliographic record
Abstract
BACKGROUND In late December 2019, a new pandemic caused by the SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infection, began to spread around the world. The new situation gave rise to severe health threats, economic uncertainty, and social isolation, causing potential deleterious effects on the physical and mental health of the people. OBJECTIVE This study aimed to evaluate worries, anxiety and depression in the public during the initial Coronavirus Disease 2019 (COVID-19; Coronavirus) pandemic lockdown in three culturally different communities: Middle Eastern (Israel), European (Poland) and North American (Canada). METHODS A cross-sectional online anonymous survey was conducted simultaneously in Israel, Poland and Canada during the lockdown periods in these countries. The survey included a demographic questionnaire, a questionnaire on original personal concerns regarding the Coronavirus pandemic and the Patient Health Questionnaire-4 (PHQ-4) which is a brief screening tool used for assessing anxiety and depression. A total of 2207 people successfully completed the survey. The data obtained from the survey were statistically analysed. RESULTS The results of the survey showed that Poles were the most concerned about being infected by the virus, with higher scores found among women and elders. Canadians worried the most about their finances, relations with relatives and friends and both physical and mental health, while Poles, despite being the most concerned about virus contamination, worried the least about their physical health and Israeli worried the least about their mental health and relations with relatives and friends. Canadians obtained the highest total score in PHQ-4, as well as in both the anxiety and depression subscales of the questionnaire, while the scores of Israelis were the lowest. All the findings were statistically significant. CONCLUSIONS The study showed that various factors should be considered while formulating appropriate solutions in emergency circumstances such as a pandemic. Understanding these factors will aid in the development of response strategies to mitigate the adverse effects of stress, social isolation and uncertainty on the well-being and mental health of culturally different societies.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".