Targeted Educational Intervention Through Game-Based Learning to Promote Rational Antimicrobial Use Among Health Care Learners: Prospective Interventional Study
Bibliographic record
Abstract
Background: Antimicrobial resistance (AMR) is a global problem. Training health care professionals in the rational use of antimicrobials is essential to curb AMR. Objective: To support efforts to reduce antibiotic resistance, this study assesses how well a gamified educational intervention might improve health care professionals' and students' understanding and use of appropriate antibiotics. Methods: This is a prospective interventional study conducted for clinical practitioners, undergraduates (MBBS and interns), postgraduates, and pharmacy students. A total of 60 participants were included in the study. Innovative games were administered to support the management of infections across multiple body systems, in accordance with the 2022 Indian Council of Medical Research treatment guidelines and the latest Infectious Diseases Society of America guidelines, incorporating multiple instructional components. Pretest and posttest questionnaires were administered and evaluated. Results: After the intervention, participants' ability to differentiate between bacterial and viral symptoms in respiratory tract infections and gastroenteritis improved from 48% to 94%. The practice of selecting the appropriate empirical antimicrobial at the correct dose, route, and duration also demonstrated significant improvement, reflecting enhanced adherence to principles of rational antimicrobial use. Conclusions: The gamified intervention successfully improved participants' knowledge and awareness of rational antimicrobial use. Substantial improvements across all the assessed components highlight the positive impact of the intervention in promoting optimal antimicrobial use and curbing AMR. Innovative gamified interventions may foster better and longer-lasting awareness, supporting appropriate antimicrobial use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".