Engagement in Distance Healthcare Simulation Debriefing
Bibliographic record
Abstract
SUMMARY STATEMENT: Understanding distance health care simulation debriefing is crucial in light of the increased use of and emerging technology in remote education for reasons of accessibility, global collaboration, and continuous professional development. This article is a confluence of a number of previously published studies designed to serve as a foundation to develop the concept of "engagement in health care distance simulation debriefing" using the Schwartz-Barcott & Kim hybrid mixed methods model. The model uses 3 phases: theoretical (a realist systematic review of the literature), fieldwork (3 exploratory studies and 2 pilot experimental studies), and analytical (analysis of the theoretical and fieldwork findings through expert discussion). This study defines the concept of "engagement in health care simulation distance debriefing" through exploration of its uses and analysis in literature, interviews, and expert review. The hybrid approach to the analysis provided rigor to generate a new, reflective conceptual model. This conceptual model defines the complexity in engagement during distance debriefing and helps shape the development of simulationists and debriefers, leading to more effective distance simulations and debriefings.
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 | 0.087 | 0.240 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".