The outbreak of coronavirus disease in China: Risk perceptions, knowledge, and information sources among prenatal and postnatal women
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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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 | 0.000 | 0.000 |
| 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".