Perceptions, healthcare messaging and its impact on COVID vaccine uptake in pregnancy : A cross-sectional survey
Bibliographic record
Abstract
Objective: To gain in-depth insights into factors affecting COVID-19 vaccine acceptance in pregnant women. This demographic has lower rates of COVID vaccination despite being disproportionally negatively affected by COVID. Design: A single centre cross-sectional online survey distributed 13th August 2021 to 21st September 2021 on local networks. Setting: Online Population: Pregnant population in a large District General Hospital in the West Midlands, UK. Main Outcome Measures: i) demographic and baseline data ii) awareness of information sources; iii) opinions on COVID and vaccination, iv) vaccination decisions. Results: 92 total eligible responses were quantitatively and qualitatively analysed. 60.9% (n=56) had declined, or would decline, a COVID-19 vaccination. Those who had a previous negative pregnancy experience were significantly more likely to accept a COVID-19 vaccination (OR 3.9; p<0.05, 95% CI 1.32-11.52). Over half (53.2%) of participants either agreed or strongly agreed that discussion with a healthcare professional was important in decision making on vaccination . GPs were the least supportive of the vaccination (62.5%) compared to midwives (78.8%) and obstetric consultants (81.8%). The most common reason for declining the vaccine were perceived risks to the fetus; this was also frequently reported in the qualitative analysis. Other qualitative themes included; distrust of recommendations, conflict of information and uncertainty. Conclusions: There is a need for consistent health professional messaging around vaccine uptake in pregnancy and the use of appropriate evidence based information, particularly focusing on its safety and impact on fetus. There is a need to nurture a collaborative approach and informed decision making.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".