Side Effects May Include Fun: Pre- and Post-Market Surveillance of the GridlockED Serious Game
Bibliographic record
Abstract
Introduction. As serious games are a relatively new phenomenon in medical education, there is little data on end user demographics or usage. In this study our goal was to describe the demographics and usage for purchasers of the GridlockED board game, a serious board game for teaching about a systems approach to managing care in the emergency department. Methods. We conducted a two-phase survey of individuals interested in purchasing GridlockED. Users were asked to complete a brief demographic survey before accessing the purchasing site. A follow-up survey was performed 3-6 months after the initial survey. That survey was to assess participants’ usage, play patterns, and what changes to GridlockED they would like to see. Individuals who did not purchase the board game were asked about their barriers to purchase. Results. After one year of sales, 213 games were purchased, 560 individuals had completed the intake survey with 408 consented to follow-up. Responding purchasers were from 16 different roles in healthcare in 11 countries. Our follow-up survey collated 53 responses (out of 408 individuals, 14% response rate). The majority (63%) of respondents reported having played the game, with the most common use cases being for fun (40%), teaching trainees (21%) or training with colleagues (13%). Price of the game unit was cited as the largest barrier to purchase (60%). Conclusion. GridlockED attracted interest from a wide range of medical professionals around the world. Users reported using the game for fun and for teaching/training purposes. The main barrier to purchase was the game’s price.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".