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Record W7045825205

Barriers and Facilitators to Arts Participation in Older Adults

2022· article· en· W7045825205 on OpenAlexaboutno aff

Bibliographic record

VenueSOURCE Sheridan's Institutional Repository (Sheridan College) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsVariety (cybernetics)Community participationKey (lock)Older peopleQuality of life (healthcare)
DOInot available

Abstract

fetched live from OpenAlex

Despite a growing literature showcasing the benefits of arts participation for older adults, data on potential barriers to participation are limited. A survey was developed to assess key facilitators and barriers to participation in the arts in Canadian older adults. One hundred and ninety-four residents of Ontario, Canada aged 55 years+ completed the survey. Almost half of the participants (45%) were participating in the arts, with the most common activities in the domains of visual arts and dance. Barriers to participation included lack of interest in available programs, cost, and not wanting to participate alone. Facilitators to participation included a wider variety of offerings that were either low cost or free, and improved accessibility to activities. Numerous strategies including lower costs, improved transit access, virtual onboarding, and forming a buddy system could further improve participation levels and encourage more older adults to capitalize on the numerous potential health benefits of the arts.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.646

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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