Understanding the Effects of Health Care Distance Simulation
Bibliographic record
Abstract
ABSTRACT: The use of distance simulation has rapidly expanded in recent years with the physical distance requirements of the COVID-19 pandemic. With this development, there has been a concurrent increase in research activities and publications on distance simulation. The authors conducted a systematic review of the peer-reviewed distance health care simulation literature. Data extraction and a risk-of-bias assessment were performed on selected articles. Review of the databases and gray literature reference lists identified 10,588 titles for review. Of those, 570 full-text articles were assessed, with 54 articles included in the final analysis. Most of these were published during the COVID-19 pandemic (2020-2022). None of the included studies examined an outcome higher than a Kirkpatrick level of 2. Most studies only examined low-level outcomes such as satisfaction with the simulation session. There was, however, a distinction in studies that were conducted in a learning environment where all participants were in different locations ("distance only") as compared with where some of the participants shared the same location ("mixed distance"). This review exclusively considered studies that focused solely on distance. More comparative studies exploring higher level outcomes are required to move the field forward.
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 | 0.020 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.000 |
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".