The outbreak of coronavirus disease in China: Risk perceptions, knowledge, and information sources among prenatal and postnatal women
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has created anxiety among members of the public, including all women over the childbirth continuum, who are considered to be at a greater risk of contracting most infectious diseases. Understanding the perspectives of health care consumers on COVID-19 will play a crucial role in the development of effective risk communication strategies. This study aimed to examine COVID-19-related risk perceptions, knowledge, and information sources among prenatal and postnatal Chinese women during the initial phase of the COVID-19 pandemic. METHODS: A cross-sectional survey design was adopted, and a four-section online questionnaire was used to collect data. Using a social media platform, the online survey was administered to 161 participants during the outbreak of COVID-19 in Nanjing, China, in February 2020. RESULTS: The participants perceived their risk of contracting and dying from COVID-19 to be lower than their risk of contracting influenza, however many of them were worried that they might contract COVID-19. The participants demonstrated adequate knowledge about COVID-19. The three major sources from which they obtained information about COVID-19 were doctors, nurses/midwives, and the television, and they placed a high level of confidence in these sources. There was no significant relationship between the perceived risk of contracting COVID-19 and knowledge about this disease. CONCLUSION: The present findings offer valuable insights to healthcare professionals, including midwives, who serve on the frontline and provide care to pregnant women. Although the participants were adequately knowledgeable about COVID-19, they had misunderstood some of the recommendations of the World Health Organisation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it