Facilitator development for pre-registration health professions simulation: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The growing demand for health professional education intensifies the need for learning innovations such as simulation: facilitating predictable, realistic, experiential learning that prepares students for practice. To achieve this, facilitators must provide pedagogically sound, psychologically safe simulation. High-quality simulation enhances students' self-efficacy, critical thinking, and clinical reasoning. Despite increasing integration of simulation into curricula, best practices for facilitator development remain unknown, risking the quality and safety of simulations. OBJECTIVE: This scoping review will identify the extent and type of evidence guiding the development of simulation facilitators in pre-registration health professional programs for any type and stage of simulation. INCLUSION CRITERIA: This review will consider reports on simulation facilitator development for educators of pre-registration health professional students in academic settings. The simulation may be delivered using any delivery modality and in any language. Reports focused on simulation facilitators working in professional settings, within graduate programs, or with already licensed learners will be excluded. METHODS: The review will follow the JBI methodology for scoping reviews. The databases to be searched will include CINAHL (EBSCOhost), Embase, ERIC (EBSCOhost), MEDLINE (Ovid), and ProQuest Dissertation and Theses, from 2005 to the present. Titles and abstracts, followed by full-text articles, will be screened by 2 independent reviewers. Data will be extracted using a pre-defined data extraction form and content analysis will be conducted. Extracted data will be presented using tables, charts, and a narrative summary. REVIEW REGISTRATION: Open Science Framework: https://osf.io/wf9zc.
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 | 0.139 | 0.110 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.069 | 0.018 |
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".