A Serious Game (Immunitates) About Immunization: Development and Validation Study (Preprint)
Bibliographic record
Abstract
BACKGROUND 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. OBJECTIVE We aimed to develop and validate a serious game about immunization and vaccination for Brazilian nursing students. METHODS 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. RESULTS 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. CONCLUSIONS 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.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".