Use of narratives to enhance childhood vaccine acceptance: Results of an online experiment among Canadian parents
Bibliographic record
Abstract
Identifying effective interventions to promote children's vaccination acceptance is crucial for the health and wellbeing of communities. Many interventions can be implemented to increase parental awareness of the benefits of vaccination and positively influence their confidence in vaccines and vaccination services. One potential approach is using narratives as an intervention. This study aims to evaluate the effects of a narrative-based intervention on parents' attitudes and vaccination intentions. In a pre-post experiment, 2,000 parents of young children recruited from an online pan-Canadian panel were randomly exposed to one of the three videos presenting narratives to promote childhood vaccination or a control condition video about the importance and benefits of physical activity in children. Pre-post measures reveal a relatively modest but positive impact of the narratives on parents' attitudes and intention to vaccinate their child(ren). The results also suggest that narratives with more emotional content may be more effective in positively influencing vaccine attitudes than the more factual narrative. Using narratives to promote vaccination can positively influence parents' views and intentions toward childhood vaccines, but research is still required to identify the best components of such interventions.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".