OUP accepted manuscript
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is little understanding about how rapid urban development has affected the extent to which communities are able to optimize health and participation opportunities for older adults in Hong Kong. Our objective was to examine what older residents perceive to be the shortcomings of their communities in meeting their psychosocial and physical needs as they age. RESEARCH DESIGN AND METHODS: In reference to the WHO Age-Friendly Cities Project Methodology: Vancouver Protocol, we conducted nine focus groups comprising 65 participants for an Age-Friendly City baseline assessment in two districts in Hong Kong, China. Participants were asked to share their views on their respective district of residence, and identify aspects of the city they found unfriendly. Data generated from interviews were analyzed using thematic analysis. RESULTS: Five of the following key themes were identified: the failure of public transportation to cater to the needs of older adults; a lack of public space for recreation and socializing; diminishing human interactions in welfare services; physical and financial challenges relating to housing; and workplace discrimination against older adults. DISCUSSION AND IMPLICATIONS: These findings underscore the importance of prioritizing the social welfare of older adults in building a more inclusive and age-friendly city. They also highlight the difficulties in fostering an inclusive environment while ensuring efficiency and profit maximization.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.787 | 0.644 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".