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Record W2153285186 · doi:10.1016/s1726-4901(09)70232-1

Prevalence and Correlates of Depressive Symptoms in Older Taiwanese Immigrants in Canada

2005· article· en· W2153285186 on OpenAlexaffabout
Daniel W. L. Lai

Bibliographic record

VenueJournal of the Chinese Medical Association · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineImmigrationDepressive symptomsDepression (economics)GerontologyClinical psychologyPsychiatryDemographyAnxiety

Abstract

fetched live from OpenAlex

BACKGROUND: There is a lack of research regarding depression in older Taiwanese immigrants in North American countries. This study in Canada therefore examined the prevalence of depressive symptoms among older immigrants from Taiwan, and psychosocial factors as predictors of depressive symptoms reported by older Taiwanese immigrants. METHODS: Ninety-eight migrants (aged > or = 55 years) from Taiwan to Canada, who were part of a multi-site study of health and well-being in a total of 2,272 older ethnic Chinese individuals in community dwellings, completed a face-to-face interview and answered questions in an orally administrated questionnaire. Depressive symptoms were measured by a Chinese version of the Geriatric Depression Scale. RESULTS: Of the 98 migrants from Taiwan, 21.5% reported at least a mild level of depression. Predictive factors for depressive symptoms were a negative attitude towards aging, poor general physical health, single marital status, barriers in terms of gaining access to health care services, poor financial status, lower level of identification with Chinese health beliefs, and low income. CONCLUSION: The prevalence of depressive symptoms in older Taiwanese immigrants in Canada was higher than that reported by older adults in the general Canadian population. Thus, implications for the delivery of health care services, and possible strategies to enhance the mental well-being of older Taiwanese immigrants, are discussed.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.721
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.002
GPT teacher head0.232
Teacher spread0.230 · 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

Citations35
Published2005
Admission routes2
Has abstractyes

Explore more

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