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Record W3162263828 · doi:10.1177/07334648211015456

Digital Storytelling in Older Adults With Typical Aging, and With Mild Cognitive Impairment or Dementia: A Systematic Literature Review

2021· review· en· W3162263828 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Applied Gerontology · 2021
Typereview
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of WaterlooUniversity of Alberta
FundersConsortium canadien en neurodégénérescence associée au vieillissement
KeywordsDementiaCognitive impairmentStorytellingGerontologyCognitionPsychologyCognitive agingSystematic reviewClinical psychologyMedicineMEDLINEPsychiatryDiseaseNarrative

Abstract

fetched live from OpenAlex

The rates of dementia are on the rise as populations age. Storytelling is commonly used in therapies for persons living with dementia and can be in the form of life review, and reminiscence therapy. A systematic literature review was conducted to examine the range and extent of the use of digital technologies for facilitating storytelling in older adults and their care partners, and to identify the processes and methods, the technologies used and their readiness levels, the evidence, and the associated outcomes. Eight electronic databases were searched: Medline, EMBASE, PsycINFO, CINAHL, Abstracts in Social Gerontology, ERIC, Web of Science, and Scopus. We included 34 studies. Mild cognitive impairment or dementia represented over half of medical conditions reported in the studies. Overall, our findings indicate that the most common use of digital storytelling was to support older adults’ memory, reminiscence, identity, and self-confidence; however, the level of evidence of its effectiveness was low.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.114
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.044
GPT teacher head0.379
Teacher spread0.335 · 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