Pediatric COVID-19 vaccine hesitancy in Canada: An analysis of Tweets (Preprint)
Bibliographic record
Abstract
BACKGROUND In July 2022, a COVID-19 vaccine was approved for use in Canada for children between the ages of 6 months to five years old. Although COVID-19 vaccine uptake has been strong in the adult population, only 6.5% of the youngest members of society have been vaccinated in Canada. OBJECTIVE This research aimed to determine the reasons behind why some parents and caregivers might feel hesitant towards vaccinating their young ones. Knowing the reasons why parents and caregivers might feel hesitant about vaccinating their child could help doctors communicate more effectively with their concerned patients or identify current misinformation needing to be dispelled by public health agencies. METHODS Researchers obtained data (i.e., Tweets) from Twitter the week following the Health Canada approval of a COVID-19 vaccine for young children. These Tweets were qualitatively analyzed for themes regarding vaccine hesitancy. RESULTS Of the 1,192 Tweets that were extracted and analyzed, 449 Tweets (38%) had expressions of vaccine hesitancy. Within those Tweets, the main themes were centred around: safety concerns, the belief that children are not impacted by COVID-19, political or economic concerns, and the belief that the vaccine is not effective. CONCLUSIONS Compared to similar research on vaccine hesitancy for adults, two additional themes were distinct when discussing the vaccine for children: children were not viewed as impacted by COVID-19 and the belief that the vaccine would not be effective.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".