A Serious Game (Health Unit in Focus) for Enhancing Undergraduate Education on Older Adults’ Health: Design and Validation Study
Bibliographic record
Abstract
Background: Population aging underlines the critical need to improve health professional training to adequately care for adults aged >60 years. Developing educational resources to support academics and professionals presents a valuable opportunity to enhance understanding of health conditions and improve clinical management. Serious games are designed to develop teaching, training, and learning skills. Their use in the educational setting is warranted, as they integrate digital aspects and gamification to create a playful experience for content acquisition. Deepening this theme in nursing education will improve assistance to the older adult population, leading to more qualified care based on gerontological practices and comprehensive health care for older adults. Objective: This study aims to develop and validate a serious game on older adult health for undergraduate nursing students. Methods: This quantitative and descriptive methodological study, conducted between February 2023 and December 2023 at a public university in the Federal District of Brazil, involved the active participation of 27 undergraduate nursing students in their eighth to tenth semesters. The game, Health Unit in Focus (HUF), was developed and validated with their input. It features 75 clinical cases distributed across 3 themes: pharmacology, metabolic syndrome, and semiology. Of the 40 students initially enrolled, 27 completed the study. The app was validated using the System Usability Scale and student feedback, and the results were reported following the Game-Based Intervention Reporting Guidelines (GAMING). Results: The participants had a mean age of 22.67 (SD 1.44) years, were mostly female (20/27, 74%), and were in their eighth semester (26/27, 96%). The game received an average System Usability Scale score of 85.75 (median 86.57), classified as excellent, as all evaluated items scored >75. Participants considered the game easy to use; accessible; practical; and rich in well-founded, useful content. This high usability score, coupled with the overwhelmingly positive feedback from the students, instills confidence in the game's effectiveness. Furthermore, 100% (27/27) of students agreed that learning through games is effective and expressed interest in incorporating more interactive games into their training. The serious game HUF showed good usability, as its overall score was "excellent," with its highest score in the odd-numbered items that addressed the positive aspects identified in the analysis. Conclusions: The serious game HUF is not just a valid and reliable tool for training nursing students but also an engaging and interactive approach to learning. Its ability to captivate and involve students in the learning process is a testament to its potential to revolutionize nursing education. It is essential that the development of new methodological resources, such as serious games, be based on scientific evidence to guarantee greater reliability and success in achieving their established objectives.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".