Facilitators, barriers and impacts to implementing dementia care training for staff in long-term care settings by using fully immersive virtual reality: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The rapid growth of the ageing population underscores the critical need for dementia care training among care providers. Innovative virtual reality (VR) technology has created opportunities to improve dementia care training. This scoping review will specifically focus on the barriers, facilitators and impacts of implementing fully immersive VR training for dementia care among staff in long-term care (LTC) settings. METHODS AND ANALYSIS: We will follow the Joanna Briggs Institute's scoping review methodology to ensure scientific rigour. We will collect literature of all languages with abstracts in English from CINAHL, Medline, Scopus, Embase, Web of Science and ProQuest database until 31 December 2023. Grey literature from Google Scholar and AgeWell websites will be included. Inclusion criteria encompass papers involving paid staff (Population), fully immersive VR training on dementia care (Concept) and LTC settings (Context). Literature referring only to non-paid caregivers, non-fully immersive VR or other chronic diseases will be excluded. Literature screening, data extraction and analysis will be conducted by two reviewers separately. We will present a narrative summary with a charting table on the main findings. ETHICS AND DISSEMINATION: This work does not require ethics approval, given the public data availability for this scoping review. Through a comprehensive overview of the current evidence regarding impacts, barriers and facilitators on this topic, potential insights and practical recommendations will be generated to support the implementation of VR training to enhance staff competence in LTC settings. The findings will be presented in a journal article and shared with practitioners on the frontline.
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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 | 0.120 | 0.089 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.044 | 0.011 |
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".