Examining an Altruism-Eliciting Video Intervention to Increase COVID-19 Vaccine Intentions in Younger Adults: A Qualitative Assessment Using the Realistic Evaluation Framework
Bibliographic record
Abstract
COVID-19 vaccine-induced immunity wanes over time, and with the emergence of new variants, additional "booster" doses have been recommended in Canada. However, booster vaccination uptake has remained low, particularly amongst younger adults aged 18-39. A previous study by our research team found that an altruism-eliciting video increased COVID-19 vaccination intentions. Using qualitative methods, the present study aims to: (1) identify the factors that influence vaccine decision-making in Canadian younger adults; (2) understand younger adults' perceptions of an altruism-eliciting video designed to increase COVID-19 vaccine intentions; and (3) explore how the video can be improved and adapted to the current pandemic context. We conducted three focus groups online with participants who: (1) received at least one booster vaccine, (2) received the primary series without any boosters, or (3) were unvaccinated. We used deductive and inductive approaches to analyze data. Deductively, informed by the realist evaluation framework, we synthesized data around three main themes: context, mechanism, and intervention-specific suggestions. Within each main theme, we deductively created subthemes based on the health belief model (HBM). For quotes that could not be captured by these subthemes, additional themes were created inductively. We found multiple factors that could be important considerations in future messaging to increase vaccine acceptance, such as feeling empowered, fostering confidence in government and institutions, providing diverse (such as both altruism and individualism) messaging, and including concrete data (such as the prevalence of vulnerable individuals). These findings suggest targeted messaging tailored to these themes would be helpful to increase COVID-19 booster vaccination amongst younger adults.
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How this classification was reachedexpand
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".