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Record W4312038904 · doi:10.1093/geroni/igac059.2805

IMPLEMENTING VISUAL VIDEOS AND IMAGES WITH PEOPLE WITH DEMENTIA IN CARE SETTINGS: A SCOPING REVIEW

2022· review· en· W4312038904 on OpenAlexaff
Karen Lok, Yi Wong, Mario Bayani, Jim Mann, Annette Berndt, Lily Wong, Carly Wang, Diane Pan, Lillian Hung

Bibliographic record

VenueInnovation in Aging · 2022
Typereview
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLonelinessDementiaPsychologyCognitionIsolation (microbiology)PopulationApplied psychologySocial psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

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 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)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.868
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.350
GPT teacher head0.628
Teacher spread0.279 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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