ADRENALINE, a Learning Game to Improve Prescribing Skills in Undergraduate Medical Students: Descriptive Study
Bibliographic record
Abstract
Background: Junior doctors often demonstrate insufficient prescribing skills, highlighting the need to enhance undergraduate medical training in this area. Serious games (SGs) have proven effective in teaching knowledge and skills across various medical specialties including surgery and emergency care. To our knowledge, no SG specifically dedicated to prescribing has been developed to date. Our objective was to develop a new educational program, based on a learning game designed to enhance medical students' competencies in safe and effective prescribing. Objective: This study aimed to describe ADRENALINE, a learning game designed to promote safe and effective prescribing, and to report feedback from sixth-year undergraduate medical students at our medical school after the first year of its implementation in the therapeutics curriculum. Methods: This study implemented an interactive educational program based on Kolb experiential learning theory to enhance safe and effective prescribing skills among sixth-year medical students. The program followed 3 phases: a preliminary in-person lecture introducing the SG ADRENALINE, autonomous gameplay, and a final debriefing lecture. ADRENALINE, accessible via university platform Moodle (Andrews Lyons) on multiple devices, was developed using MOSAIC software (Katia Quelennec), a software program created to develop evolutive SG based on real-life professional situations, and includes 20 realistic clinical scenarios of varying difficulty, requiring students to make therapeutic decisions and receive immediate feedback. Players advance through levels based on performance, with ongoing support from professors via feedback and a dedicated forum. The program was integrated into the therapeutic curriculum of Lille University, and participation was voluntary. All 598 sixth-year students were invited to access the game via email and to participate in pre- and postintervention surveys assessing usage patterns, satisfaction, and learning outcomes. Results: Between November 2023 and March 2024, 272 sixth-year students accessed the ADRENALINE program. Of these, 201/272 (73.9%) students completed at least one scenario and obtained scores ranging from 16.5 to 100 out of 100. Pretest survey responses (n=99 answers) indicated that 92/99 (93%) students identified as gamers and believed that SGs could be relevant for their medical education. Posttest survey responses (n=50 answers) reflected a high level of satisfaction among participants. Most students reported that ADRENALINE helps them apply academic knowledge in real-world context, feel more confident with prescribing and managing adverse drug reactions, improve their prescribing skills, and better prepare for the national Objective Structured Clinical Examination. Conclusions: We developed a learning game focused on medical prescribing, designed to be easily shared with other French-speaking medical schools. Although only 201/598 (33.6%) students engaged with this initial version, 85% (42/50) of the feedback received was positive, indicating strong student interest and supporting the educational value of a game-based approach to enhance prescribing skills among undergraduate medical students.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".