Chambre des erreurs : d'une réalité régionale au parcours immersif à 360°
Bibliographic record
Abstract
Room of horrors are medical simulation tools created 12 years ago in Canada. They allow learners to identify patient care errors (hygiene, patient welfare, drugs…) intentionally added in a reconstituted patient room. These risk management tools allowed the training of tens of thousands of caregivers about risks associated with care giving. They also helped raise awareness among inpatients and users. However, the implementation of these rooms of horrors requires significant human and material resources to renew the scenarios. Moreover, the novelty effect eroding over time, we must think about revised concepts, considering the digital transition of education and health systems. IatroMed 360° is an innovative solution ofroom of horrors focused on medication errors. This serious game produced by the “association pour le digital et l’information en pharmacie” (ADIPh) used 360° virtual tour and virtual reality. With a smartphone, a tablet or a computer, the learner is immersed in a care unit and has to identify 18 medication errors. The serious-game has been published in 2016. It has been used for initial and continuous training of more than 300 learners, all over the French territory. The satisfaction of learners and trainers confirms the need to sustain the tool and to consider possible variations: a similar room, focused on oncology care and a chemotherapy unit, SimUPAC 360 °, was created in 2017. Other tools are being developed and should help combat adverse events associated with hospitalisation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".