A Serious Game (Immunitates) About Immunization: Development and Validation Study (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Vaccination is a fundamental part of all levels—local to worldwide—of public health, and it can be considered one of humanity's greatest achievements in the control and elimination of infectious diseases. Teaching immunization and vaccination can be monotonous and tiring. It is necessary to develop new approaches for teaching these themes in nursing school. </sec> <sec> <title>OBJECTIVE</title> We aimed to develop and validate a serious game about immunization and vaccination for Brazilian nursing students. </sec> <sec> <title>METHODS</title> We developed a quiz-type game, Immunitates, using design and educational theoretical models and Brazilian National Health Guidelines. The game’s heuristics and content were evaluated with 2 different instruments by a team of experts. A sample of nursing students evaluated the validity of the game’s heuristics only. We calculated the content validity index (CVI) for each evaluation. </sec> <sec> <title>RESULTS</title> The study included 49 experts and 15 nursing students. All evaluations demonstrated high internal consistency (Cronbach α≥.86). The game’s heuristics (experts: CVI 0.75-1.0; students: CVI 0.67-1.0) and the game’s contents demonstrated validity (experts: CVI 0.73-1.0). Participants identified some specific areas for improvement in the next version. </sec> <sec> <title>CONCLUSIONS</title> The serious game appears to be valid. It is intended as a support tool for nursing students in the teaching–learning process and as a tool for continuing education for nurses. </sec>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".