MétaCan
Menu
Back to cohort
Record W2290382558 · doi:10.1080/01634372.2015.1118716

Strengthening Social Capital Through Residential Environment Development for Older Chinese in a Canadian Context

2015· article· en· W2290382558 on OpenAlexaffabout
Hai Luo

Bibliographic record

VenueJournal of Gerontological Social Work · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSocial capitalSocial environmentContext (archaeology)PsychologyFocus groupPopulation ageingQualitative researchSocial supportPopulationGerontologySociologySocial psychologyGeographyMedicineDemographySocial science

Abstract

fetched live from OpenAlex

Among Canada's visible-minority population 65 years of age or older, nearly four out of ten are Chinese. However, little research has been devoted to the examination of the role of the housing environment in building social capital for older Chinese despite the increase in this population and related social issues. The purpose of this paper is to examine Chinese elders' experience of social capital and how it is affected by their residential environment in a Canadian context. In this qualitative study, forty-three Chinese elders in a Canadian context were interviewed with a focus group approach. Findings indicate that the environments in which these older adults lived either hindered or assisted them in building or increasing their social capital. A culturally and linguistically homogeneous residential environment does not necessarily provide positive support to older Chinese for their acquisition of social capital. Adversities in the environment, such as maltreatment or lack of support from their immediate micro environment (family), tended to motivate older adults to improve their social capital for problem-solving. The study offers implications from research findings to social work practice and concludes with an analysis of limitations.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.068
GPT teacher head0.354
Teacher spread0.287 · 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 designObservational
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

Citations11
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Gerontological Social WorkSame topicHealth disparities and outcomesFrench-language works237,207