A Serious Game Designed to Promote Safe Behaviors Among Health Care Workers During the COVID-19 Pandemic: Development of “Escape COVID-19”
Bibliographic record
Abstract
BACKGROUND: As many countries fear and even experience the emergence of a second wave of COVID-19, reminding health care workers (HCWs) and other hospital employees of the critical role they play in preventing SARS-CoV-2 transmission is more important than ever. Building and strengthening the intrinsic motivation of HCWs to apply infection prevention and control (IPC) guidelines to avoid contaminating their colleagues, patients, friends, and relatives is a goal that must be energetically pursued. A high rate of nosocomial infections during the first COVID-19 wave was detected by IPC specialists and further cemented their belief in the need for an engaging intervention that could improve compliance with COVID-19 safe behaviors. OBJECTIVE: Our aim was to develop a serious game that would promote IPC practices with a specific focus on COVID-19 among HCWs and other hospital employees. METHODS: The first 3 stages of the SERES framework were used to develop this serious game. A brainswarming session between developers and IPC specialists was used to identify the target audience and acquisition objectives. Nicholson's RECIPE mnemonic (reflection, engagement, choice, information, play, exposition) for meaningful gamification was used to guide the general design. A common and simple terminology was used to suit the broad target audience. The game was tested on various platforms (smartphones, tablets, laptops, desktop computers) by different users during each development loop and before its final release. RESULTS: The game was designed to target all hospital staff who could be in direct contact with patients within the Geneva University Hospitals. In total, 10 acquisition objectives were defined by IPC specialists and implemented into the game according to the principles of meaningful gamification. A simple storyboard was first created using Microsoft PowerPoint and was progressively refined through multiple iteration loops. Articulate Storyline was then used to create two successive versions of the actual game. In the final version, a unique graphic atmosphere was created with help from a professional graphic designer. Feedback mechanisms were used extensively throughout the game to strengthen key IPC messages. CONCLUSIONS: The SERES framework was successfully used to create "Escape COVID-19," a serious game designed to promote safe IPC practices among HCWs and other hospital employees during the COVID-19 pandemic. This game can be obtained free of charge for research and educational purposes. A SCORM (shareable content object reference model) package is available to facilitate results and completion tracking on most current learning management systems.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".