AGE-FRIENDLY COMMUNITY STRATEGIES: A RESEARCH-BASED APPROACH ADOPTED IN GUANGZHOU, CHINA
Bibliographic record
Abstract
Making communities more “age-friendly” has been an ongoing trend since the WHO launched its global Age-Friendly Cities project. However, research on how to assess and implement age-friendly communities in China is scarce even though China has the largest number of older adults in the world. The international research collaboration between the Faculty of Social Work, University of Calgary in Canada and Guangdong Institute of Public Administration in China aims to develop an age-friendly community strategy for Guangzhou, China using a multi-method, community-based approach. We developed a quantitative baseline survey instrument using the WHO age-friendly framework, which was modified to be locally and culturally relevant. Trained interviewers administered the survey to adults 50 years of age and older in four distinct communities in Guangzhou (N = 400). Descriptive analysis was completed across items in 8 domains and comparisons were made across the four communities. Secondly, we used a series of 12 focus groups to share the preliminary findings with key stakeholders representing policy developers, service sectors and older adults in order to develop locally-relevant recommendations. This presentation will describe the findings related to the assessment of age-friendliness in Guangzhou, contribute to an increased understanding the cultural relevance of age-friendly communities, and identify strategies of developing age-friendly communities that are locally and culturally relevant.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".