Adapting an Effective Health-Promoting Intervention—Choose to Move—for Chinese Older Adults in Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".