Attitudes of pregnant women toward vaccination against COVID-19 - a study conducted in Poland in the first quarter of 2022
Bibliographic record
Abstract
OBJECTIVES: The study aimed to assess the attitudes of pregnant women toward vaccination against COVID-19. METHODS: The research was conducted using a diagnostic survey with our original questionnaire among 283 pregnant women. The survey was carried out in Poland in the first quarter of 2022. Statistical analyses were performed using IBM SPSS 26.0 (p < 0.05). RESULTS: It was shown that 140 (49.5%) pregnant women were vaccinated against COVID-19, of which 90 (64.3%) received vaccination during pregnancy. In the group of 143 (50%) unvaccinated people, only 11.9% of respondents expressed willingness to be vaccinated against COVID-19. The most frequently cited arguments for receiving the vaccine were fear of a severe course of the disease (37.5%) and the possibility of passing antibodies to a child (37.1%). Women who did not undergo vaccination believed that they did not want to put themselves and their babies at risk (39.9%) and were concerned about adverse post-vaccination reactions (35.2%) and the safety of the vaccine (32.5%). Women with higher education and professionally active (p = 0.004) were vaccinated more often than respondents with a lower level of education (p < 0.001). Age (p = 0.101) and place of residence (p = 0.179) did not indicate statistically significant differences in decision-making regarding vaccination against COVID-19. CONCLUSION: Pregnant women presented both pro- and anti-vaccination attitudes. Less than half of the respondents were vaccinated against COVID-19, and most of the women took the preparation during pregnancy. Selected socio-demographic factors determined women's attitudes toward vaccinations against COVID-19. Medical personnel should play a role in deciding whether a pregnant woman is vaccinated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.010 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".