Virtual clinical simulation to teach mental health concepts: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to determine the extent of the literature on the use of virtual clinical simulation to teach health professional students about mental health. INTRODUCTION: Graduates of health professional programs need to be prepared to provide safe and effective care for persons with a mental illness in every practice context. Clinical placements in specialty areas are difficult to obtain and cannot ensure students will have opportunities to practice specific skills. Virtual simulation is a flexible and innovative tool that can be used in pre-registration health care education to effectively develop cognitive, communication, and psychomotor skills. Given the recent focus on virtual simulation usage, the literature will be mapped to determine what evidence exists regarding virtual clinical simulation to teach mental health concepts. INCLUSION CRITERIA: We will include reports that focus on pre-registration health professional students and use virtual simulation to teach mental health concepts. Reports that focus on health care workers, graduate students, patient viewpoints, or other uses will be excluded. METHOD: Four databases will be searched including MEDLINE, CINAHL, PsycINFO, and Web of Science. Reports with a focus on mental health virtual clinical simulation for health professional students will be mapped. Independent reviewers will screen titles and abstracts, then review the full texts of articles. Data from studies meeting the inclusion criteria will be presented in figures and tables, and described narratively. REVIEW REGISTRATION NUMBER: Open Science Framework https://osf.io/r8tqh.
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 | 0.091 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".