Development of Serious Games (Equit’Game) to Address Health and Environmental Inequalities: Protocol for an App-Delivered Program to Perform a Territorial Diagnosis
Bibliographic record
Abstract
BACKGROUND: Territorial diagnosis is a prerequisite for local actions concerning public health and for the reduction of social, environmental, and health-related inequalities. To orient local programs or initiatives targeting health inequalities, policymakers need a simulation of territorial diagnosis tools. Yet, very few platforms have been developed for the purpose of guiding public authorities as they seek to reduce these social inequalities. OBJECTIVE: This study aimed to describe the design and methods of the development process of a territorial diagnosis tool based on a serious game named Equit'Game that puts learners at the heart of the territorial diagnosis process, asking them to review the current state of health, environmental state, and socioeconomic state of their territory. METHODS: The realistic situations employed in our serious game should encourage players, in a fun and playful manner, to (1) appropriate the data of their own territory, (2) apply their methodological knowledge in a practical way, (3) reflect on the most pertinent statistical or spatial tools for their situation, and (4) ultimately, to acquire new knowledge and skills in the use of territorial diagnosis tools with a spatial dynamic. Equit'Game was deployed over the course of a week's training and structured into 4 levels: level 1, Dataminer (identifying relevant information to respond to the question); level 2, Analyst (selecting the appropriate method of analysis); level 3, Atlas (mapping the data); and level 4, Cluster (extraction of statistical and spatial information). Equit'Game has also been designed as a sort of virtual campus, creating a fun learning environment in which each door represents a level. Users can access Equit'Game via a platform compatible with tablets, PCs, and mobile phones. RESULTS: In the first step, we tested our application interface designed especially for adults among a panel of local health professionals. The following are some of the most relevant points: font size and colors used, voice accompaniment in texts and messages guiding the user, clear and easy interfaces, and the change between successive game levels. In the second step, we used our application, Equit'Game, with postgraduate students from the School of Public Health (École des hautes études en santé publique). At the end of the game session, we conducted a satisfaction survey, including several items covering both the application interface and the execution of the game. CONCLUSIONS: Equit'Game was developed to help learners with the techniques of territorial diagnosis, with the aim of creating an innovative tool for public health capable of conveying educational messages and providing a structure for training. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/11786.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.085 | 0.021 |
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".