Impact of a Serious Game on the Intention to Change Infection Prevention and Control Practices in Nursing Homes During the COVID-19 Pandemic: Protocol for a Web-Based Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Nursing home residents are at high risk of complications and death due to COVID-19. Lack of resources, both human and material, amplifies the likelihood of contamination in these facilities where a single employee can contaminate dozens of residents and colleagues. Improving the dissemination of and adhesion to infection prevention and control (IPC) guidelines is therefore essential. Serious games have been shown to be effective in developing knowledge and in increasing engagement, and could motivate nursing home employees to change their IPC practices. OBJECTIVE: Our aim is to assess the impact of "Escape COVID-19," a serious game designed to enhance knowledge and application of IPC procedures, on the intention of nursing home employees to change their IPC practices. METHODS: We will carry out a web-based randomized controlled trial following the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) guidelines and incorporating relevant elements of CHERRIES (Checklist for Reporting Results of Internet E-Surveys). Participants will be randomized to either the control or the serious game (intervention) group. First, both groups will be asked to answer a questionnaire designed to gather demographic data and assess baseline knowledge. The control group will then receive a quick reminder of the current national guidelines and links to IPC guidelines for health care professionals, while the other group will play the game. Both groups will then have to answer a second questionnaire designed to assess their willingness to change their IPC practices after having followed their respective material. After completing this questionnaire, they will be granted access to the material presented to the group they were not assigned to and receive a course completion certificate. The primary outcome will be the proportion of participants willing to change their IPC practices according to group. Secondary outcomes will include the analysis of specific questions detailing the exact changes considered by the participants. Factors associated with participant willingness or reluctance to change behavior will also be assessed. Attrition will also be assessed at each stage of the study. RESULTS: The study protocol has been presented to our regional ethics committee (Req-2020-01262), which issued a declaration of no objection as such projects do not fall within the scope of the Swiss federal law on human research. Data collection began on November 5, 2020, and should be completed by December 4, 2020. CONCLUSIONS: This study should determine whether "Escape COVID-19," a serious game designed to improve compliance with COVID-19 safe practices, modifies the intention to follow IPC guidelines among nursing home employees. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25595.
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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 | 0.042 | 0.048 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.078 | 0.011 |
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".