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Record W4281550476 · doi:10.36150/2499-6564-n407

Barriers and facilitators to older adults’ engagement in healthy aging initiatives

2022· article· en· W4281550476 on OpenAlexaff
Amber Hastings‐Truelove, Setareh Ghahari, Angela Coderre-Ball, Dorothy Kessler, Jennifer Turnnidge, Britney Lester, Mohammad Auais, Nancy Dalgarno, Vincent DePaul, Catherine Donnelly, Marcia Finlayson, Diana Hopkins‐Rosseel, Klodiana Kolomitro, Kathleen E. Norman, Trisha L. Lawson, Denise Stockley, Richard van Wylick, Kevin Woo

Bibliographic record

VenueJournal of Gerontology and Geriatrics · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsQueen's University
FundersPfizer
KeywordsCINAHLPsycINFOMEDLINEHealthy agingDemographicsGerontologyRehabilitationGerontological nursingMedicineCochrane LibraryGeriatric rehabilitationPsychologyNursingAlternative medicinePhysical therapyPsychological intervention

Abstract

fetched live from OpenAlex

Objectives. To identify the facilitators and barriers to older adults’ participation in healthy aging or cardiovascular rehabilitation programs. Methods. We conducted a scoping review to identify healthy aging program evaluations which identified participant barriers and facilitators. We developed a search strategy in the following databases: MEDLINE, Embase, APA PsycInfo, and Cochrane CENTRAL, all on the Ovid platform and Ebsco CINAHL.Results. We included 17 articles in this review. Our team categorized the barriers and facilitators of older adults’ participation in healthy aging programs into seven themes: attitudes, organizational structure, accessibility, social structure, knowledge, demographics, and program specifics.Conclusions. Understanding the facilitators and barriers that older adults face when deciding whether or not to participate or to continue participating in, healthy aging programs to promote in, cardiovascular health can help healthcare professionals provide optimal guidance for their patients and clients.

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)0.042
metaresearch head score (Gemma)0.130
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.130
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.342
Teacher spread0.320 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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Same venueJournal of Gerontology and Geriatrics→Same topicCardiac Health and Mental Health→French-language works237,207→