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Record W7017348124

Are age friendly communities also resilient communities?

2013· article· en· W7017348124 on OpenAlexaboutno aff

Bibliographic record

VenueNorthumbria Research Link (Northumbria University) · 2013
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionHealth carePerceptionSocial capitalFace (sociological concept)Test (biology)Focus groupPublic policy
DOInot available

Abstract

fetched live from OpenAlex

In both absolute and relative terms the number of older people is increasing globally, and while everyone hopes to stay active and healthy as they age, seniors face particular challenges to maintaining health, and consequently make more use of healthcare services than any other age group. But the challenges faced by individual seniors and to our health care system can be mitigated by policy interventions that promote seniors’ health. In this paper we focus on one such policy that has generated global interest: Age-Friendly Communities (AFC). According to the World Health Organization these are communities, “where policies, services and structures related to the physical and social environment [that] are designed to support and enable older people … to live in security, enjoy good health and continue to participate fully in society”. Specifically we present the findings of a collaborative study of the perceptions of residents–old and young–regarding age-friendliness of one Canadian city, St John’s, the capital city of the Canadian province of Newfoundland and Labrador. Our study represents the first to test whether residents’ impressions of AFC characteristics differ by age.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.002
Open science0.0060.003
Research integrity0.0000.002
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 head0.077
GPT teacher head0.289
Teacher spread0.212 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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