Effects of Simulation on Nursing students' Knowledge and Learning Related to Measles Vaccine and Vaccine Hesitancy: A Mixed Method Study
Bibliographic record
Abstract
Vaccine hesitancy is a persistent, global public health concern that community health nurses are well-positioned to manage. Simulations involving standardized patients are effective experiential learning on managing vaccine hesitancy for other allied health disciplines. A pretest-posttest design, with a mixed-methods, one-group, quasi-experimental approach, was used to examine the effectiveness of a simulation on nursing students' knowledge of measles, mumps, and rubella vaccine hesitancy, as well as their attitudes toward the intervention. The study was completed with 61 participants. After participation in the simulation, pretest/posttest data showed a mean increase in participants' knowledge of the measles, mumps, and rubella vaccine. There was a significant improvement in the test scores from 62.62 ± 14.82 to 69.50 ± 15.75; z = -3, 897 (1-17 days) (p = 0.001). A postintervention questionnaire revealed participants most appreciated the direct interaction with a live person, the opportunity to observe classmates' performance and share feedback, multistage structure, and safety. Drawbacks included stress from being observed by peers, time constraints, and the necessity of sharing the nursing role with a partner during the scenario. Another drawback is the simulation's inability to effectively prepare participants for hesitancy in clinical settings as they needed to assess natural clinical settings. Simulations incorporating vaccine hesitancy education and standardized patients can effectively prepare nursing students for situations related to vaccine hesitancy in community clinical settings.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".