Development and Contribution of a Serious Game to Improve Nursing Students' Clinical Reasoning in Acute Heart Failure: A Multimethod Study
Bibliographic record
Abstract
Clinical reasoning is essential for nurses and nursing students to recognize and intervene when hospitalized patients present acute heart failure. Serious games are digital educational interventions that could foster the development of clinical reasoning through an engaging and intrinsically motivating learning experience. However, elements from a playful approach (eg, rewards, narrative elements) are often absent or poorly integrated in existing serious games, which may limit their contribution to learning. Thus, we developed and studied the contribution of a novel serious game on nursing students' engagement, intrinsic motivation, and clinical reasoning in the context of acute heart failure. We adopted a multimethod design and randomized 28 participants to receive two serious game prototypes in a different sequence, one that fully integrated elements of a playful approach (SIGN@L-A) and one that offered only objectives, feedback, and a functional aesthetic (SIGN@L-B). Through self-reported questionnaires, participants reported higher levels of engagement and intrinsic motivation after using SIGN@L-A. However, negligible differences in clinical reasoning scores were found after using each serious game prototype. During interviews, participants reported on the contribution of design elements to their learning. Quantitative findings should be replicated in larger samples. Qualitative findings may guide the development of future serious games.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".