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Record W2086212446 · doi:10.1093/sw/52.3.261

Effects of Service Barriers on Health Status of Older Chinese Immigrants in Canada

2007· article· en· W2086212446 on OpenAlexaffabout
Daniel W. L. Lai, Shirley Chau

Bibliographic record

VenueSocial Work · 2007
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsImmigrationEthnic groupService providerService (business)Language barrierMental healthSocioeconomic statusGerontologyPerceptionOlder peoplePsychologyService delivery frameworkMedicineBusinessEnvironmental healthPopulationGeographySociologyPolitical scienceMarketingPsychiatry

Abstract

fetched live from OpenAlex

The authors examine the effects of service barriers on the health status of older Chinese immigrants in Canada. A survey was completed in seven Canadian cities by a random sample of 2,214 older Chinese immigrants age 55 years or older. Service barriers related to administrative problems, personal attitudes, and circumstantial difficulties were significant predictors of physical and mental health when controlling for the demographic factors. Empirically, the findings confirm that service barriers are detrimental to the health of older immigrants. The service barriers in the areas of ethnic, language, or cultural differences between the service providers or services themselves and the older Chinese clients also suggest that factors related to communication contribute to these older clients' perception of services or providers as culturally insensitive or unresponsive. Considering the individual, social, and economic costs incurred by adverse health consequences, barriers in service delivery must be addressed.

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)0.005
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 score0.067
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.310
Teacher spread0.303 · 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

Citations85
Published2007
Admission routes2
Has abstractyes

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