MétaCan
Menu
← Back to cohort
Record W6999785818

Do primary care providers who speak Chinese improve access to mental health care of Chinese immigrants?

2009· article· en· W6999785818 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed Central · 2009
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthOdds ratioLogistic regressionHealth careDepression (economics)Confidence intervalImmigrationPopulation
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The utilization of health care providers who share the language and culture of their patients has been advocated as a strategy to improve access to the mental health care of immigrants. This study examines the relationship between patients receiving primary care from health care providers who speak Chinese and the rate of mental health diagnosis and consultation among Chinese immigrants in British Columbia (BC), Canada. METHODS: The study analyzed 3 linked administrative databases: an immigration database, BC’s health databases and BC’s physician register. The study population consisted of more than 270 000 recent Chinese immigrants to BC, with sex and age-matched comparison subjects. We calculated the odds ratios (ORs) of being diagnosed with common mental health conditions and the rate ratios (RRs) of mental health visits per year of health plan registration, by proportion of general care received from Chinese-speaking physicians; this was done using logistic regression and generalized linear models, adjusting for sex, age and time registered in the health plan. RESULTS: Among Chinese immigrants, a higher proportion of care received from Chinese-speaking general practitioners (GPs) was associated with a lower probability of being diagnosed with neurotic disorders (OR = 0.87; 95% confidence interval [CI] 0.80–0.95), drug dependence (OR = 0.22; 95% CI 0.14–0.35), adjustment reaction (OR = 0.39; 95% CI 0.33–0.46) and depressive disorder not elsewhere classified (OR = 0.47; 95% CI 0.42–0.52), as well as a lower rate of mental health service utilization (RR = 0.65%; 95% CI 0.61–0.69). Among the comparison group, a higher proportion of primary care received from Chinese-speaking GPs was associated with a lower probability of being diagnosed with affective psychoses (OR = 0.53; 95% CI 0.47–0.59), neurotic disorders (OR = 0.49; 95% CI 0.47–0.51), drug dependence (OR = 0.28; 95% CI 0.24–0.32), acute reaction to stress (OR = 0.54; 95% CI 0.51–0.57), adjustment reaction (OR = 0.36; 95% CI 0.33– 0.39), depressive disorder not elsewhere classified (OR = 0.30; 95% CI 0.29–0.32) and anxiety/depression (OR = 0.83; 95% CI 0.80–0.86), and with lower rates of mental health service utilization (RR = 0.32; 95% CI 0.30–0.33). CONCLUSIONS: Although Chinese-speaking primary care physicians may facilitate Chinese immigrants’ access to medical care, these physicians may not optimize diagnosis and treatment of mental health problems. Our findings have implications for access to mental health care by minority populations in metropolitan centres in Canada and North America, where immigrants rely heavily on health care practitioners who speak their native language for their primary care.

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)0.007
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 score0.358
Threshold uncertainty score0.711

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.305
Teacher spread0.295 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

Same venuePubMed Central→Same topicMigration, Health and Trauma→French-language works237,207→