COVID-19 vaccine uptake and intention during pregnancy in Canada
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: To investigate COVID-19 vaccine uptake and intent among pregnant people in Canada, and determine associated factors. METHODS: We conducted a national cross-sectional survey among pregnant people from May 28 through June 7, 2021 (n = 193). Respondents completed a questionnaire to determine COVID-19 vaccine acceptance (defined as either received or intend to receive a COVID-19 vaccine during pregnancy), factors associated with vaccine acceptance, and rationale for accepting/not accepting the vaccine. RESULTS: Of 193 respondents, 57.5% (n = 111) reported COVID-19 vaccine acceptance. Among those who did not accept the vaccine, concern over vaccine safety was the most commonly cited reason (90.1%, n = 73), and 81.7% (n = 67) disagreed with receiving a vaccine that had not been tested in pregnant people. Confidence in COVID-19 vaccine safety (aOR 16.72, 95% CI: 7.22, 42.39), Indigenous self-identification (aOR 11.59, 95% CI: 1.77, 117.18), and employment in an occupation at high risk for COVID-19 exposure excluding healthcare (aOR 4.76, 95% CI: 1.32, 18.60) were associated with vaccine acceptance. Perceived personal risk of COVID-19 disease was not associated with vaccine acceptance in the multivariate model. CONCLUSION: Vaccine safety is a primary concern for this population. Safety information should be communicated to this population as it emerges, along with clear messaging on the benefits of vaccination, as disease risk is either poorly understood or poorly valued in this population.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it