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Record W3071079323 · doi:10.1080/08882746.2020.1805265

Aging in community: the case of Hesperus village in Vaughan in Ontario, Canada

2020· article· en· W3071079323 on OpenAlexaffabout
Lauren Kalvari

Bibliographic record

VenueHousing and Society · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsYork University
Fundersnot available
KeywordsSociologyPublic relationsDemocracyService (business)BarterQualitative researchPolitical scienceSocial scienceBusinessPoliticsMarketing

Abstract

fetched live from OpenAlex

This study focuses on the evolving notion of aging in community, by presenting findings of a qualitative, descriptive study conducted in Hesperus Village, a unique community for predominantly older adults located in Vaughan in Ontario, Canada. The purpose of this exploratory study was to gauge the benefits and challenges of aging in community, and develop conceptualization of the nature and form of attached social relationships and community concerns. Eight semi-structured, in-depth interviews with open-ended questions of residents and a manager were conducted. Findings indicated that aging in this community included a mix of resident co-caring and formal caring practices; that traditional forms of management style are shifting to democratic self-governance practices and that forging links with the wider community is linked to the sharing of resources and inter-generational bartering of services. The implications of aging in community point toward the notion of social responsibility as a potential strategy toward sustainable practices; and that sharing resources with the wider community may have cost-saving implications. This study serves as a call for further research and advocacy in addressing an all-encompassing service delivery model to age in community from birth until death, linking aging in place and dying in place agendas.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.401

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.328
Teacher spread0.279 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2020
Admission routes2
Has abstractyes

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