Social Media Misinformation about Pregnancy and COVID-19 Vaccines: A Systematic Review
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
OBJECTIVE: The objectives of this study were to identify common social media misconceptions about COVID-19 vaccination in pregnancy, explain the spread of misinformation, and identify solutions to guide clinical practice and policy. METHODOLOGY: A systematic review was conducted and the databases Embase and Medline were searched from December 2019 to February 8, 2023, using terms related to social media, pregnancy, COVID-19 vaccines and misinformation. The inclusion criteria were original research studies that discussed misinformation about COVID-19 vaccination during pregnancy on social media. The exclusion criteria were review articles, no full text, and not published in English. Two independent reviewers conducted screening, extraction, and quality assessment. RESULTS: Our search identified 76 articles, of which 3 fulfilled eligibility criteria. Included studies were of moderate and high quality. The social media platforms investigated included Facebook, Google Searches, Instagram, Reddit, TikTok, and Twitter. Misinformation was related to concerns regarding vaccine safety, and its association with infertility. Misinformation was increased due to lack of content monitoring on social media, exclusion of pregnant women from early vaccine trials, lack of information from reputable health sources on social media, and others. Suggested solutions were directed at pregnancy care providers (PCPs) and public health/government. Suggestions included: (i) integrating COVID-19 vaccination information into antenatal care, (ii) PCPs and public health should increase their social media presence to disseminate information, (iii) address population-specific vaccine concerns in a culturally relevant manner, and others. CONCLUSION: Increased availability of information from reputable health sources through multiple channels could increase COVID-19 vaccine uptake in the pregnant population and help combat misinformation.
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.
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 | 0.006 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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