IMPLEMENTING VISUAL VIDEOS AND IMAGES WITH PEOPLE WITH DEMENTIA IN CARE SETTINGS: A SCOPING REVIEW
Bibliographic record
Abstract
Abstract There is limited literature on using visual videos and images with people with dementia in care settings. We conducted a scoping review on this topic to fill this literature gap. Our scoping review adopted the Joanna Briggs Institute scoping review methodology. We eventually included eleven papers for the review and conducted the content analysis. We found the facilitators for implementing visual videos and images with people with dementia in care settings: 1. Matching people’s interests 2. Being congruent with people’s cognitive abilities 3. Support from families and staff 4. Using in a group setting. We also found the barriers: 1. Staff is unwilling to support 2. Lack of resources 3. Not congruent with the cognitive or other abilities of the people. We found benefits of using visual videos and images with this population: 1. Encourage expression 2. Facilitate discussions with other people 3. Improve well-being. We also found drawbacks: the potential of arousing negative emotions and memories. We suggest future research should include the voices of people with dementia, staff should be trained to support the people in case negative memories and emotions are aroused, and there should be consideration of using visual videos and images to tackle isolation and loneliness in care settings. With these findings, this scoping review should shed light on implementing visual videos and images in care settings.
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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".