Serious Game for the Nursing Assessment of Home-Dwelling Older Adults: Development and Validation Study
Bibliographic record
Abstract
BACKGROUND: The use of serious games (SGs) in nursing education is increasing, with the COVID-19 pandemic significantly accelerating their development. A key feature of SGs is their flexibility, allowing students to train at any place and time as needed. Recently, there has been a shift from developing disease-specific SGs to games focused on broader health issues. However, there has been a lack of proposals to enhance nursing interventions in home and frail care settings. The REACtion project developed a SG to improve students' understanding and clinical reasoning in caring for home-dwelling older adults. OBJECTIVE: This study aims to describe the development of "REACtion Game" (RG) and explore its validity as an educational tool. A multidisciplinary team created a SG that simulates the assessment process of older adults in home settings by nurses. It features web-based scenarios, clickable objects, and a menu with tools, and medical records to enhance nursing students' knowledge and clinical reasoning skills. METHODS: A prospective, observational study was conducted using the Dutch Society for Simulation in Healthcare's framework to validate the game. Further, 5 experts in home health care nursing evaluated content validity, while 30 students assessed construct validity, face validity, concurrent validity (by comparing game scores with those from the Nursing Clinical Reasoning Scale), game quality, and usability. Data were collected through self-administered web-based questionnaires and the debriefings of each match played. The students were enrolled in 2 postgraduate nursing programs: a master of science in nursing degree and a first-level continuing education in family and community nursing. RESULTS: Experts rated the content validity highly after revisions (universal agreement calculation method of scale-level content validity index=0.97). The sample consisted of 30 students, predominantly women (n=20, 67%) and aged younger than 45 years (n=23, 77%) with no prior experience in SG. Almost all students had a positive impression of RG as an attractive and useful method for learning new knowledge. Participants found the cases, scenarios, and dialogues realistic (face validity) and of high quality, though usability aspects such as instructions clarity and intelligibility of game progression were less favored. Construct validity showed general agreement on the game's educational value, with family and community nursing students reporting more consistent alignment with educational goals. Overall, RG scores correlated positively with time spent playing but showed limited correlation with Nursing Clinical Reasoning Scale scores. CONCLUSIONS: This study developed and validated a nursing education game, especially valuable as simulation is underused in some curricula. Created during the pandemic, it offered a digital learning environment. Although the game shows potential, further testing is needed for usability, concurrent validity, and functional improvements. Future research should involve larger samples to fully validate the game and assess its impact on academic achievement.
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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 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".