Writing Technical Reports for Simulation in Education for Health Professionals: Suggested Guidelines
Bibliographic record
Abstract
Simulation is an important training tool used in a variety of influential fields. However, development of simulation scenarios - the key component of simulation - occurs in isolation; sharing of scenarios is almost non-existent. This can make simulation use a costly task in terms of the resources and time and the possible redundancy of efforts. To alleviate these issues, the goal is to strive for an open communication of practice (CoP) surrounding simulation. To facilitate this goal, this report describes a set of guidelines for writing technical reports about simulation use for educating health professionals. Using an accepted set of guidelines will allow for homogeneity when building simulation scenarios and facilitate open sharing among simulation users. In addition to optimizing simulation efforts in institutions that are currently using simulation as an educational tool, the development of such a repository may have direct implications on developing countries, where simulation is only starting to be used systematically. Our project facilitates equivalent and global access to information, knowledge, and highest-caliber education - in this context, simulation - collectively, the building blocks of optimal healthcare.
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 | 0.167 | 0.485 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.041 | 0.072 |
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".