Fear about adverse effect on fertility is a major cause of COVID‐19 vaccine hesitancy in the United States
Bibliographic record
Abstract
Although COVID-19 vaccine access has increased nationwide, vaccination rates have been slow-moving, with many studies showing significant vaccine hesitancy in the U.S. We conducted an online survey using Amazon Mechanical Turk (MTurk) to identify reasons for vaccine hesitancy among unvaccinated adults between June 30 and July 1, 2021. We found that 58% of unvaccinated respondents were worried about unknown long-term adverse effects. Of these, 41% believed that the COVID-19 vaccines can negatively impact reproductive health and or fertility, and 38% were unsure of the effects on fertility. Our study demonstrates that fear regarding COVID-19 vaccine adverse effects and belief that they can negatively impact fertility is a major cause of vaccine hesitancy in the United States. We identified that urban residents, married individuals, those born outside the U.S., those with health insurance, and people with higher education and income greater than $100,000 felt that the vaccine would affect fertility more than their counterparts did. Finally, we found that 48% of unvaccinated respondents cited 'more information and research conducted on the COVID-19 vaccines' as the action that would most encourage vaccine uptake.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".