Development and evaluation of virtual simulation games to increase the confidence and self-efficacy of healthcare learners in vaccine communication, advocacy, and promotion
Bibliographic record
Abstract
BACKGROUND: Although healthcare providers (HCPs) are the most trusted source of vaccine information, there is a paucity of easily accessible, multidisciplinary educational tools on vaccine communication for them. Virtual simulation games (VSGs) are innovative yet accessible and effective tools in healthcare education. The objectives of our study were to develop VSGs to increase HCP confidence and self-efficacy in vaccine communication, advocacy, and promotion, and evaluate the VSGs' effectiveness using a pre-post self-assessment pilot study. METHODS: A multidisciplinary team of experts in medicine, nursing, pharmacy, and simulation development created three VSGs for HCP learners focused on addressing conversations with vaccine hesitant individuals. We evaluated the VSGs with 24 nursing students, 30 pharmacy students, and 18 medical residents who completed surveys and 6-point Likert scale pre-post self-assessments to measure changes in their confidence and self-efficacy. RESULTS: There were no significant differences in baseline confidence and self-efficacy across the three HCP disciplines, despite varied levels of education. Post-VSG confidence and self-efficacy (median: 5) were significantly higher than pre-VSG (median: 4-5) for all three HCP disciplines (P ≤ 0.0005), highlighting the effectiveness of the VSGs. Medical residents reported significantly lower post-VSG confidence and self-efficacy than nursing and pharmacy learners despite completing the most significant amount of education. CONCLUSIONS: Following the completion of the VSGs, learners in medicine, nursing, and pharmacy showed significant improvement in their self-assessed confidence and self-efficacy in holding vaccine conversations. The VSGs as an educational tool, in combination with existing clinical immunization training, can be used to increase HCP confidence and engagement in vaccine discussions with patients, which may ultimately lead to increased vaccine confidence among patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".