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Record W2943619367 · doi:10.1080/17533015.2019.1608567

Using the arts for awareness, communication and knowledge translation in older adulthood: a scoping review

2019· review· en· W2943619367 on OpenAlexfundno aff
Mandy M. Archibald, Alison Kitson

Bibliographic record

VenueArts & Health · 2019
Typereview
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
FundersNational Medical Research CouncilNational Health and Medical Research CouncilCanadian Institutes of Health ResearchHealth Canada
KeywordsThe artsContext (archaeology)NarrativeKnowledge translationPsychologyIdentification (biology)Applied psychologyMedical educationKnowledge managementVisual artsMedicineComputer scienceArtGeography

Abstract

fetched live from OpenAlex

BACKGROUND: The arts are powerful methods of enhancing social engagement and well-being in older adulthood. Literature on arts utility in translating knowledge about ageing and related processes is emerging but poorly understood. We conducted a scoping review to map research on how the arts are used for awareness, communication, and knowledge translation in older adulthood. METHODS: We consulted a research librarian, comprehensively searched four interdisciplinary databases, systematically screened 1321 articles and extracted data from 11 included articles. RESULTS: Articles predominantly originated from the Health Sciences, were informed by qualitative data, and were developed linearly, from problem identification to art development. Performance theatre was the most commonly employed narrative approach. CONCLUSIONS: Approaches to arts development in this context do not maximize collaboration and participant engagement, thereby reducing potential impacts of arts for older persons. We propose a cyclical and collaborative alternative to developing arts strategies for combined communicative and engagement purposes.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0130.012
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.586
GPT teacher head0.611
Teacher spread0.025 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations35
Published2019
Admission routes1
Has abstractyes

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