Student Preferences on Gaming Aspects for a Serious Game in Pharmacy Practice Education: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Serious games are motivating and provide a safe environment for students to learn from their mistakes without experiencing any negative consequences from their actions. However, little is known about students' gaming preferences and the types of serious games they like to play for education. OBJECTIVE: This study aims to determine the types of gaming aspects that students would like to play in a pharmacy-related serious game. METHODS: A cross-sectional study was conducted using a self-administered survey, which obtained students' responses on their preferences regarding various gaming aspects (reward systems, game settings, storylines, viewing perspectives, and gaming styles) and for a hypothetical gaming scenario (authentic simulation or post-apocalyptic fantasy). Descriptive statistics, chi-square, and Fisher's exact tests were used for statistical analyses. RESULTS: Response rate was 72.7% (497/684 undergraduates). The most popular game reward systems were unlocking mechanisms (112/497, 22.5%) and experience points (90/497, 18.1%). Most students preferred fantasy/medieval/mythic (253/497, 50.9%) and modern (117/497, 23.5%) settings, but lower year undergraduates preferred modern settings less than upper year seniors (47/236, 19.9% vs 70/242, 28.9%, P=.022). Almost one-third (147/497, 29.6%) preferred an adventurer storyline or an authentic pharmacy-related plot (119/497, 23.9%), and a collaborative game style was most preferred by the students (182/497, 36.6%). Three-dimensional game perspectives (270/497, 54.3%) were more popular than two-dimensional perspectives (221/497, 44.5%), especially among males than females (126/185, 68.1% vs 142/303, 46.9%, P<.001). In terms of choice for a pharmacy-related serious game, a post-apocalyptic fantasy game (scenario B, 287/497, 57.7%) was more popular than an authentic simulation game (scenario A, 209/497, 42.1%). More males preferred the post-apocalyptic fantasy scenario than females (129/187, 69.0% vs 155/306, 50.7%, P<.001). CONCLUSIONS: In general, students want a three-dimensional, fantasy/medieval/mythic post-apocalyptic game, based on an adventurer storyline with an unlocking mechanism reward system. A balance between real-life and fantasy elements needs to be struck in order for the game to cater students towards health care practices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".