Design of a Serious Game for Handling Obstetrical Emergencies
Bibliographic record
Abstract
BACKGROUND: The emergence of new technologies in the obstetrical field should lead to the development of learning applications, specifically for obstetrical emergencies. Many childbirth simulations have been recently developed. However, to date none of them have been integrated into a serious game. OBJECTIVE: Our objective was to design a new type of immersive serious game, using virtual glasses to facilitate the learning of pregnancy and childbirth pathologies. We have elaborated a new game engine, placing the student in some maternity emergency situations and delivery room simulations. METHODS: A gynecologist initially wrote a scenario based on a real clinical situation. He also designed, along with an educational engineer, a tree diagram, which served as a guide for dialogues and actions. A game engine, especially developed for this case, enabled us to connect actions to the graphic universe (fully 3D modeled and based on photographic references). We used the Oculus Rift in order to immerse the player in virtual reality. Each action in the game was linked to a certain number of score points, which could either be positive or negative. RESULTS: Different pathological pregnancy situations have been targeted and are as follows: care of spontaneous miscarriage, threat of preterm birth, forceps operative delivery for fetal abnormal heart rate, and reduction of a shoulder dystocia. The first phase immerses the learner into an action scene, as a doctor. The second phase ask the student to make a diagnosis. Once the diagnosis is made, different treatments are suggested. CONCLUSIONS: Our serious game offers a new perspective for obstetrical emergency management trainings and provides students with active learning by immersing them into an environment, which recreates all or part of the real obstetrical world of emergency. It is consistent with the latest recommendations, which clarify the importance of simulation in teaching and in ongoing professional development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".