South Asian Youth as Vaccine Agents of Change (SAY-VAC): evaluation of a public health programme to mobilise and empower South Asian youth to foster COVID-19 vaccine-related evidence-based dialogue in the Greater Toronto and Hamilton Area, Canada
Bibliographic record
Abstract
OBJECTIVES: There have been substantial amounts of misinformation surrounding the importance, safety and effectiveness of the COVID-19 vaccine. The impacts of this misinformation may be augmented as they circulate among ethnic communities, who may concurrently face other barriers related to vaccine uptake and access. To combat some of the key sources of COVID-19 vaccine misinformation among the South Asian communities of the Greater Toronto and Hamilton Area (GTHA), an interdisciplinary team of researchers and marketing experts established the South Asian Youth as Vaccine Agents of Change (SAY-VAC) programme to support and empower South Asian youth to disseminate COVID-19 vaccine information. DESIGN: Cross-sectional and one-group pretest-post-test design. SETTING: GTHA. PARTICIPANTS: South Asian youth (18-29 years). INTERVENTION: The team partnered with grass-roots South Asian organisations to collaborate on shared objectives, curate key concerns, create video products regarding the COVID-19 vaccine that would resonate with the community, disseminate the products using established social media channels and evaluate the effectiveness of this effort. OUTCOMES: We assessed the change in self-reported knowledge about the COVID-19 vaccine and participant confidence to facilitate a conversation around the COVID-19 vaccine using pre-post surveys, after the implementation of the SAY-VAC programme. RESULTS: In total, 30 South Asian youth (median age=23.2 years) from the GTHA participated in the programme. After completing the SAY-VAC programme, participants reported an increase in their self-reported knowledge regarding the COVID-19 vaccine from 73.3% to 100.0% (p=0.005), and their self-reported confidence to have a conversation about the vaccine with their unvaccinated community members increased from 63.6% to 100.0% (p=0.002). Overall, 51.9% of the participants reported being able to positively affect an unvaccinated/community member's decision to get vaccinated. CONCLUSIONS: The SAY-VAC programme highlights the importance of community partnerships in developing and disseminating culturally responsive health communication strategies. A constant assessment of the evidence and utilisation of non-traditional avenues to engage the public are essential.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".