Constructing a Multidisciplinary Network That Relies on Disruptive Technologies to Design, Test, and Implement Simulation Training
Bibliographic record
Abstract
MaxSIMhealth is a multidisciplinary network of manufacturing, design, and simulation labs at Ontario Tech University combining expertise in health sciences, business and information technology (IT), and engineering while building community partnerships to advance simulation training. It discovers existing simulation gaps, provides innovative solutions that change systems, and leads to improved healthcare outcomes. Specifically, it utilizes disruptive technologies, including 3D printing, gaming, and extended reality, as innovative solutions that deliver cost-effective, portable, and realistic simulation, which is currently lacking. MaxSIMhealth is a novel collaborative innovation with aims to develop future cohorts of scholars with strong competencies ranging from technology application, to collaborating in new environments, communicating professionally, and problem-solving. Its work will transform current health professional education landscapes by providing novel, flexible, and inexpensive simulation environments. This editorial aims to showcase maxSIMhealth's innovative strategy focusing on collaborations of expertise in order to develop new simulation solutions that advance the health industry.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".