The use and impact of virtual reality programs supported by aromatherapy for older adults: A scoping review protocol
Bibliographic record
Abstract
Both virtual reality and aromatherapy have shown promising impacts on the health and well-being of older adults. Aromatherapy has been reported to enhance immersive experiences during virtual reality programs. However, studies on the combined use and impact of virtual reality and aromatherapy for older adults have not been systematically reviewed. Therefore, this scoping review will identify existing types of virtual reality programs supported by various forms of aromatherapy and their outcome measures and results on the well-being of older adults. This review will be conducted in accordance with the Joanna Briggs Institute methodology or scoping reviews and will be reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The search strategy will encompass both published and unpublished papers. The databases to be searched are CINAHL, MEDLINE, Embase, Scopus, Web of Science, ACM digital library, IEEE Xplore digital library, Compendex, ProQuest, and Google Scholar. Two independent reviewers will perform title and abstract screening, full-text screening, and data extraction. Data analysis and synthesis will be discussed by the whole research team, mapped in the literature table and accompanied by a narrative summary. Scoping review data will be collected from publicly available articles; research ethics approval is not required. The findings will be disseminated through a peer-reviewed publication and conference presentations.
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 | 0.075 | 0.062 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.012 |
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".