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Use of Dental Care by Elderly Chinese Immigrants in Canada

2007· article· en· W2063817528 on OpenAlexaffabout
Daniel W. L. Lai, Nelson T. A. Hui

Bibliographic record

VenueJournal of Public Health Dentistry · 2007
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsImmigrationMedicineDental careOral healthGerontologyHealth careLogistic regressionPromotion (chess)Family medicineGeographyPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: This study examines the predictors for elderly Chinese immigrants' use of dental care services. METHODS: In 2003, a study entitled "Health and Well Being of Older Chinese in Canada" collected data from seven cities in which 2,272 Chinese Canadians aged 55 years and older were surveyed. Data from 1,537 of these individuals were used to examine the use of dental care services by the elderly Chinese. Hierarchical logistic regression analysis was used to examine the predicting factors for use of dental care. RESULTS: More than half (52.1 percent) of the elder Chinese immigrants did not use any dental care services within the past year of the study. Being older, living in Quebec, and having poorer physical health reduced the probability that an older Chinese immigrant would use dental care services. On the other hand, being an immigrant from Hong Kong, having lived in Canada for a longer period of time, strong social support, and having dental problems increase the probability of dental service use. DISCUSSION: The findings support the need for considering the cultural characteristics and background of elderly Chinese immigrants when strengthening oral health promotion. This should encompass understanding of the holistic concept of health that includes oral health and its connections with other physical health issues.

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)0.000
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 score0.262
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.333
Teacher spread0.301 · 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

Citations38
Published2007
Admission routes2
Has abstractyes

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