Improving vaccine communication to the often-overlooked young adult population: A qualitative study of 20–29 year olds in British Columbia, Canada
Bibliographic record
Abstract
Young adults in their 20s have shown slower uptake of COVID-19 vaccines relative to older adults, potentially endangering themselves and their communities. Despite this, little vaccine communication has specifically targeted this age group. This study explored why "20-somethings" in British Columbia (BC), Canada delayed COVID-19 vaccination, and how to better encourage their vaccine uptake. From August 2022 to March 2023, we conducted semi-structured interviews with 25 young adults aged 20-29 years living in southwest BC. Interviews were recorded, transcribed, and analyzed using reflexive thematic analysis. Young adults attributed slower vaccine uptake among their demographic to the perceived lower risks to young and healthy individuals not justifying the effort involved in obtaining vaccination and to lack of trust in the vaccines' safety and effectiveness. To address these factors, participants recommended that vaccine communications attract and maintain young adults' attention, take advantage of the affordances of social media platforms, be clear about both individual advantages and social responsibility to vaccinate, and provide a way to take immediate action. Salient messengers were those deemed credible based on education/credentials or familiarity/similarity to the audience. Marginalized sub-communities may be best reached by health messengers who are already trusted community members. Tailoring vaccine communication to a young adult demographic and using trusted and relatable messengers to deliver vaccine information may facilitate swifter uptake of vaccination in settings with high access but lagging uptake.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".