Public Knowledge, Attitudes, and Practices Related to COVID-19 in Iran: Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic is a rapidly growing outbreak, the future course of which is strongly determined by people's adherence to social distancing measures. OBJECTIVE: The objective of this study was to determine the knowledge level, attitudes, and practices of the Iranian population in the context of COVID-19. METHODS: A nationwide study was conducted from March 24 to April 3, 2020, whereby data were collected via an online self-administered questionnaire. RESULTS: Responses from 12,332 participants were analyzed. Participants' mean knowledge score was 23.2 (SD 4.3) out of 30. Most participants recognized the cause of COVID-19, its routes of transmission, its symptoms and signs, predisposing factors, and prevention measures. Social media was the leading source of information. Participants recognized the dangers of the situation and felt responsible for following social distancing protocols, as well as isolating themselves upon symptom presentation. Participants' mean practice score was 20.7 (SD 2.2) out of 24. Nearly none of the respondents went on a trip, and 92% (n=11,342) washed their hands before touching their faces. CONCLUSIONS: Knowledge of COVID-19 among people in Iran was nearly sufficient, their attitudes were mainly positive, and their practices were satisfactory. There is still room for improvement in correcting misinformation and protecting people from deception. Iranians appear to support government actions like social distancing and care for their and others' safety.
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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 | 0.002 | 0.003 |
| 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".