Playing games, saving lives: a critical analysis of serious games for nursing instruction
Bibliographic record
Abstract
Used by military tacticians, political strategists, educational institutions and increasingly healthcare organizations, serious games are often defined as interactive digital games purpose-built to persuade and educate rather than strictly entertain (Zyda, 2005; Abt, 1970; Chen & Michael, 2005). Serious games offer learners the opportunity to experience subject matter in a different way than more traditional, classroom-based education. Using experiential learning theory (ELT) as defined by Kolb (1984), this Master’s Research Project (MRP) examines how two serious games, Post-Op Pediatric Clinical Simulation and Therapeutic Communication and Mental Health Assessment, Skills Practice: A Home Visit, created for nursing education, were constructed. Specifically, this paper explores how healthcare educators and technologists from Toronto-based post secondary institutions designed these serious games. Based on the designers’ understanding of serious games and their decisions, what key design elements were prioritized to support student learning and engagement? What design elements can be observed in these games? The analysis was conducted using qualitative content analysis of the designer’s interviews and qualitative content analysis of the games. The research uncovers that interactivity and immersion were observed to be prioritized by the designers in their discussions and in the final design of the game. This emphasis on interactivity and immersion was described by the designers as being in service of delivering a “real-world” simulated set of patient encounters in acute care pediatrics and mental health assessments in Post-Op and Home Visit respectively. The game designers also made a series of design decisions that resulted in an always-on, pervasive game design which encourages pick-up-and-play game replayability and student experimentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".