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Record W4387645924 · doi:10.1123/japa.2023-0064

Adapting an Effective Health-Promoting Intervention—Choose to Move—for Chinese Older Adults in Canada

2023· article· en· W4387645924 on OpenAlexaffabout
Venessa Wong, Thea Franke, Heather McKay, Catherine Tong, Heather Macdonald, Joanie Sims‐Gould

Bibliographic record

VenueJournal of Aging and Physical Activity · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsImmigrationGerontologyLonelinessEthnographyIntervention (counseling)PsychologyChinese americansMedicineNursingSociologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Evidence is sparse on how community-based health-promoting programs can be culturally adapted for racially minoritized, immigrant older adult populations. Choose to Move (CTM) is an evidence-based health-promoting program that enhances physical activity and mobility and diminished social isolation and loneliness in older adults in British Columbia, Canada. However, racially minoritized older adults were not reached in initial offerings. We purposively sampled CTM delivery staff (n = 8) from three not-for-profit organizations, in Metro Vancouver, British Columbia, that serve Chinese older adults. We used semistructured interviews, ethnographic observations, and meeting minutes to understand delivery staff's perspectives on factors that influence CTM adaptations for Chinese older adults. Deductive framework analysis guided by an adaptation framework, Framework for Reporting Adaptations and Modifications-Enhanced, found three dominant cultural- and immigration-related factors influenced CTM adaptations for Chinese older adults: (a) prioritizations, (b) familiarity, and (c) literacy. Findings may influence future program development and delivery to meet the needs of racially minoritized older adult populations.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.854
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.028
GPT teacher head0.452
Teacher spread0.424 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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