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Record W2164206412 · doi:10.1093/hsw/hls065

Effect of Service Barriers on Health Status of Aging South Asian Immigrants in Calgary, Canada

2013· article· en· W2164206412 on OpenAlexaffabout
Daniel W. L. Lai, Shireen Surood

Bibliographic record

VenueHealth & Social Work · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsImmigrationMental healthGerontologyService (business)PsychologyTelephone interviewSociocultural evolutionMedicineGeographyBusinessPsychiatrySociology

Abstract

fetched live from OpenAlex

This study examined the relationships between service barriers and health status of aging South Asian immigrants. Data were obtained through a structured telephone survey with a random sample of 220 South Asians 55 years of age and older. The effect of the different types of service barriers on the physical and mental health of participants was examined using hierarchical multiple regression, while adjusting for participants' sociocultural demographic backgrounds. An average of 5.9 types of service barriers were reported. Among the four major types of barriers--cultural incompatibility, personal attitude, administrative problems, and circumstantial challenges--more barriers related to personal attitude predicted less favorable physical and mental health. In regard to health prevention, culturally appropriate strategies should be developed and implemented to help aging South Asians to overcome barriers related to personal attitude so that they can have better access to appropriate services.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.053
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.012
GPT teacher head0.324
Teacher spread0.312 · 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

Citations47
Published2013
Admission routes2
Has abstractyes

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