Analog Serious Games for Medical Education: A Scoping Review
Bibliographic record
Abstract
PURPOSE: Serious games are increasingly used in medical education to actively engage learners. Analog serious games are a nondigital subset of serious games with specific purposes that go beyond entertainment. This scoping review describes the literature pertaining to analog serious games and provides recommendations regarding gaps and emerging directions for future research. METHOD: The authors conducted a scoping review following the Arksey and O'Malley framework, searching 3 databases (MEDLINE, Embase, and CINAHL) for studies of analog serious games designed for physician-track learners published from January 2013 through December 2023. Two authors independently screened the titles and abstracts, whereas 1 of 5 authors screened each full text and extracted data from eligible records. The authors iteratively analyzed the data within numerous categories and coded the findings to examine how the field has evolved during the past decade. RESULTS: The searches retrieved 3,955 records with 865 duplicates. The authors reviewed 3,090 title and abstract records and 202 full-text records. Eighty-eight records met the inclusion criteria, including research reports, conference abstracts, descriptive reports, and short innovation reports. The peak years for publications were 2019 and 2023 (15 publications each). Fewer abstracts and articles were published during the beginning of the COVID-19 pandemic (i.e., 2020-2022). The most common scholarship type was description studies (63 [72%]), whereas the dominant game formats were board games (51 [58%]) and card games (33 [38%]). Most studies tested analog serious games with medical students (60 [68%]) and/or residents and fellows (39 [44%]), with numerous studies including mixed study populations. CONCLUSIONS: This scoping review demonstrates moderate growth within the field of analog serious games, along with numerous opportunities for future research. Although analog game-based learning cannot entirely replace traditional pedagogical approaches, analog serious games have potential to meaningfully complement education for physician-track learners in all medical training stages.
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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 | 0.021 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.028 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".