Virtual simulation debriefing in health professions education: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to identify and map the existing literature on virtual simulation debriefing methods utilized in health professions education. INTRODUCTION: Virtual simulation has emerged as a feasible alternative to in-person learning, allowing learners to continue their education despite constraints of space, finances, and clinical placement opportunities. Virtual simulation has proven to be a vital resource for health care students during the COVID-19 pandemic. While virtual simulation provides students with continued opportunities to develop knowledge and critical thinking in a safe environment, debriefing is a crucial step for successful knowledge uptake and deeper learning. Several studies have examined this topic in various health care settings; however, there are currently no scoping reviews that have explored virtual simulation debriefing in health professions education. INCLUSION CRITERIA: This review will consider primary and secondary source articles that explore debriefing of virtual simulation within any undergraduate or graduate health education programs. Any setting that provides virtual simulation and debriefing, in any country, will be included. METHODS: The review will be conducted in accordance with JBI methodology for scoping reviews and will search the following databases: Cochrane Library, JBI Evidence-based Practice Database, MEDLINE, CINAHL, Epistemonikos, Embase, ERIC, PsycINFO, Nursing and Allied Health Database, and Web of Science. Studies published from 2016 onward will be considered. The data extracted will include specific details about the concept, context, studymethod, and critical findings relevant to the review objective. Data will be presented in diagrammatic or tabular format in a manner that aligns with the objective of this scoping review. SCOPING REVIEW REGISTRATION: Open Science Framework https://osf.io/36s5x.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".