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Record W2135601854 · doi:10.22605/rrh1118

Areca nut and betel quid chewing among South Asian immigrants to Western countries and its implications for oral cancer screening

2009· article· en· W2135601854 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRural and Remote Health · 2009
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersNational Cancer InstituteNational Institute of Dental and Craniofacial ResearchCanadian Institutes of Health Research
KeywordsArecaBetelMedicineImmigrationPopulationTraditional medicineEthnic groupEnvironmental healthHabitDentistryNutGeographyPsychologyPolitical science

Abstract

fetched live from OpenAlex

The South Asian community is the largest and one of the fastest growing minority groups in Canada, according to the 2006 census. These immigrants bring to Canada talents and skills that can promote Canada's economy and cultural diversity, but they also bring lifestyle habits that may lead to serious health issues. Chewing areca nut and betel quid (paan, with and without tobacco) is a known risk factor for oral cancer. This habit is common in the Indo-Canadian population, as evidenced by its sales in local Indian markets and restaurants. In this article, we present an overview of the sociocultural beliefs, knowledge and practices regarding betel quid/areca nut chewing, and discuss its implications for oral cancer screening among this immigrant population.

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.

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)0.000
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 score0.445
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.043
GPT teacher head0.371
Teacher spread0.328 · 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