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Record W4310566113 · doi:10.1177/07334648221142015

Digital Storytelling with Persons Living with Dementia: Elements of Facilitation, Communication, Building Relationships, and Using Technology

2022· article· en· W4310566113 on OpenAlexafffundabout
Kara Hollinda, Christine Daum, Adriana Ríos Rincón, Lili Liu

Bibliographic record

VenueJournal of Applied Gerontology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsUniversity of WaterlooUniversity of Alberta
FundersUniversity of AlbertaYuhanConsortium canadien en neurodégénérescence associée au vieillissement
KeywordsFacilitatorStorytellingDigital storytellingDementiaNarrativeFacilitationPsychologyThematic analysisQualitative researchPedagogySociologyMedicineSocial psychologyArt

Abstract

fetched live from OpenAlex

Digital storytelling is a process that can be used to co-create multimedia stories with persons living with dementia to affirm identity, support person-centered care, and leave a legacy. Although digital storytelling typically involves a facilitator, little is known about the co-creation process between a facilitator and persons living with dementia. This study explored and described elements of digital storytelling facilitation with persons living with dementia using a secondary analysis of qualitative data from a primary study that took place across three Canadian cities. Three elements were identified during digital storytelling facilitation with persons living with dementia: communicating, building collaborative relationships, and using technology. Digital storytelling facilitators employ the three elements to weave together a person's narrative with meaning. The communication, relational, and technological elements of digital storytelling may be employed by facilitators from varying professional backgrounds and lived experiences to create meaningful digital stories for persons living with dementia.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.058
GPT teacher head0.350
Teacher spread0.293 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations21
Published2022
Admission routes3
Has abstractyes

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