The Fear of COVID-19 Infection one Year After Business Reopening in Iranian Society
Bibliographic record
Abstract
Background: People’s participation in more efficient control of the disease and public awareness about the risk of COVID-19 affect their preventive behavior. This study examines the level of fear of COVID-19 infection in Iranian society after returning to social activities and business reopening. Methods: This Cross-Sectional study consisted of urban dwellers in Iran, and the data gathering tool was a researcher-designed questionnaire. To design the instrument, the authors interviewed experts and ordinary people to determine the key questions . Then, the questions were modified and finalized based on the experts’ feedback and a reexamination by the experts after two weeks. An online version of the questionnaire was disseminated using social networks. 168 people were included in the study by the available sampling method. Data were analyzed through descriptive statistics methods. Quantitative data as mean and standard deviation were reported, and the qualitative data were reported as numbers. Chi-square test and Spearman correlation coefficient were used to examine the relationship between questions related to COVID-19 infection fear and demographic variables. Data analyses were done in SPSS 20. Results: The study was carried out on 168 participants, and 78 of them were employees of different offices. The participants believed that among the ways of spreading the disease, kissing and hugging (n=142, 84.5%), shaking hands (n=127, 75.6%), contact with the saliva of an infected person (n=116, 69.0%), and spread through the air (n=60, 35.7%) had the highest frequencies. Conclusion: Fear of COVID-19 infection in the participants was at moderate and above moderate levels. The participants also hoped that the vaccine would be found and made available to the public. Policy-makers in the health sector can use the results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".