Distance simulation in the health professions: a scoping review
Bibliographic record
Abstract
BACKGROUND: Distance simulation is defined as simulation experiences in which participants and/or facilitators are separated from each other by geographic distance and/or time. The use of distance simulation as an education technique expanded rapidly with the recent COVID-19 pandemic, with a concomitant increase in scholarly work. METHODS: A scoping review was performed to review and characterize the distance simulation literature. With the assistance of an informationist, the literature was systematically searched. Each abstract was reviewed by two researchers and disagreements were addressed by consensus. Risk of bias of the included studies was evaluated using the Risk of Bias 2 (RoB 2) and Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tools. RESULTS: Six thousand nine hundred sixty-nine abstracts were screened, ultimately leading to 124 papers in the final dataset for extraction. A variety of simulation modalities, contexts, and distance simulation technologies were identified, with activities covering a range of content areas. Only 72 papers presented outcomes and sufficient detail to be analyzed for risk of bias. Most studies had moderate to high risk of bias, most commonly related to confounding factors, intervention classification, or measurement of outcomes. CONCLUSIONS: Most of the papers reviewed during the more than 20-year time period captured in this study presented early work or low-level outcomes. More standardization around reporting is needed to facilitate a clear and shared understanding of future distance simulation research. As the broader simulation community gains more experience with distance simulation, more studies are needed to inform when and how it should be used.
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 | 0.026 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.032 | 0.032 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".