Public Concerns during the COVID-19 Lockdown: A Multicultural Cross-Sectional Study among Internet Survey Respondents in Three Countries
Bibliographic record
Abstract
(1) Background: this study aimed to evaluate the worries, anxiety, and depression in the public during the initial coronavirus disease 2019 (COVID-19) pandemic lockdown in three culturally different groups of internet survey respondents: Middle Eastern (Israel), European (Poland), and North American (Canada). (2) Methods: a cross-sectional online survey was conducted in the mentioned countries during the lockdown periods. The survey included a demographic questionnaire, a questionnaire on personal concerns, and the Patient Health Questionnaire-4 (PHQ-4). A total of 2207 people successfully completed the survey. (3) Results: Polish respondents were the most concerned about being infected. Canadian respondents worried the most about their finances, relations with relatives and friends, and both physical and mental health. Polish respondents worried the least about their physical health, and Israeli respondents worried the least about their mental health and relations with relatives and friends. Canadian respondents obtained the highest score in the PHQ-4, while the scores of Israeli respondents were the lowest. (4) Conclusions: 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 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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".