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Record W3205511361 · doi:10.2196/31644

How, for Whom, and in Which Contexts or Conditions Augmented and Virtual Reality Training Works in Upskilling Health Care Workers: Realist Synthesis

2021· review· en· W3205511361 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Serious Games · 2021
Typereview
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
FundersDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsAugmented realityVirtual realityHealth careTraining (meteorology)Resource (disambiguation)Social realityPsychologySociologyBusinessComputer scienceHuman–computer interactionPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

BACKGROUND: Using traditional simulators (eg, cadavers, animals, or actors) to upskill health workers is becoming less common because of ethical issues, commitment to patient safety, and cost and resource restrictions. Virtual reality (VR) and augmented reality (AR) may help to overcome these barriers. However, their effectiveness is often contested and poorly understood and warrants further investigation. OBJECTIVE: The aim of this review is to develop, test, and refine an evidence-informed program theory on how, for whom, and to what extent training using AR or VR works for upskilling health care workers and to understand what facilitates or constrains their implementation and maintenance. METHODS: We conducted a realist synthesis using the following 3-step process: theory elicitation, theory testing, and theory refinement. We first searched 7 databases and 11 practitioner journals for literature on AR or VR used to train health care staff. In total, 80 papers were identified, and information regarding context-mechanism-outcome (CMO) was extracted. We conducted a narrative synthesis to form an initial program theory comprising of CMO configurations. To refine and test this theory, we identified empirical studies through a second search of the same databases used in the first search. We used the Mixed Methods Appraisal Tool to assess the quality of the studies and to determine our confidence in each CMO configuration. RESULTS: Of the 41 CMO configurations identified, we had moderate to high confidence in 9 (22%) based on 46 empirical studies reporting on VR, AR, or mixed simulation training programs. These stated that realistic (high-fidelity) simulations trigger perceptions of realism, easier visualization of patient anatomy, and an interactive experience, which result in increased learner satisfaction and more effective learning. Immersive VR or AR engages learners in deep immersion and improves learning and skill performance. When transferable skills and knowledge are taught using VR or AR, skills are enhanced and practiced in a safe environment, leading to knowledge and skill transfer to clinical practice. Finally, for novices, VR or AR enables repeated practice, resulting in technical proficiency, skill acquisition, and improved performance. The most common barriers to implementation were up-front costs, negative attitudes and experiences (ie, cybersickness), developmental and logistical considerations, and the complexity of creating a curriculum. Facilitating factors included decreasing costs through commercialization, increasing the cost-effectiveness of training, a cultural shift toward acceptance, access to training, and leadership and collaboration. CONCLUSIONS: Technical and nontechnical skills training programs using AR or VR for health care staff may trigger perceptions of realism and deep immersion and enable easier visualization, interactivity, enhanced skills, and repeated practice in a safe environment. This may improve skills and increase learning, knowledge, and learner satisfaction. The future testing of these mechanisms using hypothesis-driven approaches is required. Research is also required to explore implementation considerations.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.107
GPT teacher head0.445
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it