Training university students as vaccination champions to promote vaccination in their multiple identities and help address vaccine hesitancy
Bibliographic record
Abstract
Introduction: Covid-19 related vaccine hesitancy is a major problem worldwide and it risks delaying the global effort to control the pandemic. Covid-19 vaccine hesitancy is also higher in certain communities. Given that prescriber recommendation and community engagement are two effective ways of addressing vaccine hesitancy, training university students to become vaccination champions could be a way of addressing hesitancy, as the champions engage with their communities in their multiple identities. Aim: This study aims to assess the impact of a pilot project conducted in the UCL School of Pharmacy that could pave a way of integrating vaccination championing in the pharmacy undergraduate curriculum to address vaccine hesitancy. Method: Participants completed a pre-workshop questionnaire, attended an online workshop, conducted vaccination-promoting action/s, and provided evidence via a post-workshop questionnaire. Result: Fifty three students completed the course. The students’ vaccination-promoting actions ranged from speaking with vaccine-hesitant family, friends and customers in the pharmacy, to posting on various social media platforms. Post-workshop showed an increase in the knowledge of participants regarding vaccination and a decrease in the belief of vaccine misconceptions. After attending the workshop, participants were more likely to engage with vaccine-hesitant friends, family, strangers and patients. They were also more likely to receive the Covid-19 vaccine for them and for their children.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".