Characterizing preferred terms for geographically distant simulations: distance, remote and telesimulation
Bibliographic record
Abstract
Background Simulationists lack standard terms to describe new practices accommodating pandemic restrictions. A standard language around these new simulation practices allows ease of communication among simulationists in various settings. Methods We explored consensus terminology for simulation accommodating geographic separation of participants, facilitators or equipment. We used an iterative process with participants of two simulation conferences, with small groups and survey ranking. Results Small groups (n = 121) and survey ranking (n = 54) were used with distance, remote, and telesimulation as leading terms. Each was favored by a third of the participants without consensus. Conclusion This research has deepened our understanding of how simulationists interpret this terminology, including the derived themes: (1) physical distance/separation, (2) overarching nature of the term and (3) implications from existing terms. We further deepen the conceptual discussion on healthcare simulation aligned with the search of the terminologies. We propose there are nuances that prevent an early consensus recommendation. A taxonomy of descriptors specifying the conduct of distance, remote and telesimulation is preferred.
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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 | 0.038 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".