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Record W2886254880 · doi:10.1093/geront/gny052

OUP accepted manuscript

2018· article· en· W2886254880 on OpenAlexaboutno aff
CH Chui, Jennifer Tang, Christine Manlai Kwan, On Fung Chan, Michael Tse, Rebecca L. H. Chiu, VW Lou, Pui Hing Chau, Angela Yee Man Leung, Terry Yat Sang Lum

Bibliographic record

VenueThe Gerontologist · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsResidenceThematic analysisFocus groupRecreationPsychosocialWelfarePsychologySocial WelfareGerontologyEconomic growthBusinessSociologyMedicinePolitical scienceMarketingQualitative researchEconomicsSocial science

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.519
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.056
GPT teacher head0.338
Teacher spread0.281 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations30
Published2018
Admission routes1
Has abstractyes

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