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Record W2004992405 · doi:10.3138/cja.26.3.171

Relationships between Culture and Health Status: A Multi-Site Study of the Older Chinese in Canada

2007· article· en· W2004992405 on OpenAlexaffabout
Daniel W. L. Lai, Ka Tat Tsang, Neena L. Chappell, David Lai, Shirley Chau

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2007
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of TorontoUniversity of VictoriaUniversity of Calgary
Fundersnot available
KeywordsResidencePsychological interventionGerontologyActivities of daily livingPsychologyMedicineEnvironmental healthDemographySociologyNursingPhysical therapy

Abstract

fetched live from OpenAlex

This study examined the relationships between culture and the health status of older Chinese in Canada. Data were collected through face-to-face interviews with a cross-sectional, randomly selected sample of 2,272 older Chinese between 55 and 101 years of age in seven Canadian cities. Health status was assessed by the number of chronic illnesses, by limitations in ADL and IADL, and by information on the Medical Outcome Study Short Form SF-36. Although cultural variables explained only a small proportion of variance in health status, having a stronger level of identification with traditional Chinese health beliefs was significant in predicting physical health, number of illnesses, and limitations on IADL. Other cultural variables, including religion, country of origin, and length of residence in Canada, were also significant in predicting some health variables. Interventions to improve health should focus on strategies to enhance cultural compatibility between users and the health delivery system.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.282
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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

Citations55
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicChronic Disease Management StrategiesFrench-language works237,207