Community responses during early phase of the COVID-19 epidemic in the Razavi Khorasan province of Iran: a cross-sectional study
Bibliographic record
Abstract
Community responses are important for outbreak management during the early phase when preventive interventions are the major options. Therefore, this study aims to examine the behavioral responses of the community during the early phase of the A cross-sectional online survey was proceeded after confirmed COVID-19 in Iran. The research tool was demographic and risk perception questionnaire and anxiety was assessed using the 7-item GAD Scale. COVID-19 epidemic in the Razavi Khorasan Province of Iran. The population of the study was 500 residents of Razavi Khorasan areas were randomly surveyed. The data analyzed using the SPSS statistical version (V.20). The mean of age participants was 31.9±11.9. The mean GAD-7 scores were 6.4±5.2 and 92.4% had moderate or severe anxiety (GAD-7 score ≥10). Many respondents reported their health status were very good or good (62.2 %; 311/500). About a quarter of them have had respiratory symptoms in the past 14 days, and 20% of them traveled outside of the Razavi Khorasan Province in the last month. Risk perception toward COVID-19 in the community of the Razavi Khorasan Province was moderate. Most participants are alert to disease progression. This study suggested timely behavioral assessment of the community is beneficial and effective to inform next intervention, and risk communication strategies in epidemic disease.
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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 | 0.003 |
| 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) | 0.002 | 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".