Risk perception, knowledge, information sources and emotional states among COVID-19 patients in Wuhan, China
Bibliographic record
Abstract
BACKGROUND: The rapidly evolving COVID-19 pandemic has become a global health crisis. Several factors influencing risk perception have been identified, including knowledge of the disease, information sources, and emotional states. Prior studies on COVID-19-related risk perception primarily focused on the general public, with little data available on COVID-19 patients. PURPOSE: To investigate COVID-19 patients' risk perception, knowledge of the disease, information sources, and emotional states in the epicenter, Wuhan, during the COVID-19 outbreak in China. METHODS: Data were collected online using self-administered electronic questionnaire developed with reference to previous relevant studies and publications by the World Health Organization. FINDINGS: A higher level of perceived risk was found in relation to COVID-19 as compared to other potential health threats. Knowledge gaps existed regarding transmission and prevention of COVID-19. Additionally, risk perception was negatively related to knowledge and positively related to depressive states. Moreover, social media was a primary source for COVID-19 information, whereas the most trusted sources were health professionals. DISCUSSION: Realistic perception of risk should be encouraged considering both physical and mental health while developing relevant strategies. Furthermore, risk communication needs to be specifically tailored for various target groups, such as the elderly and mentally vulnerable individuals, with the adoption of popular media platforms.
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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 | 0.000 | 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".