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Record W2337263874 · doi:10.1002/cam4.700

Breast cancer screening disparities among immigrant women by world region of origin: a population‐based study in Ontario, Canada

2016· article· en· W2337263874 on OpenAlexafffundabout
Mandana Vahabi, Aïsha Lofters, Matthew Kumar, Richard H. Glazier

Bibliographic record

VenueCancer Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsPublic Health OntarioInstitute for Clinical Evaluative SciencesCentre for Global Health ResearchUniversity of TorontoToronto Metropolitan UniversitySt. Michael's Hospital
FundersCanadian Cancer Society Research InstituteUniversity of TorontoOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative Sciences
KeywordsMedicineImmigrationBreast cancer screeningMammographyCancer screeningBreast cancerPoisson regressionDemographyPap testPopulationHealth careGerontologyOutreachEthnic groupFamily medicineCancerCervical cancerEnvironmental healthGeographyCervical cancer screeningPolitical science

Abstract

fetched live from OpenAlex

Rates of mammography screening for breast cancer are disproportionately low in certain subgroups including low-income and immigrant women. The purpose of the study was to examine differences in rates of appropriate breast cancer screening (i.e., screening mammography every 2 years) among Ontario immigrant women by world region of origin and explore the association between appropriate breast cancer screening among these women groups and individual and structural factors. A cohort of 183,332 screening-eligible immigrant women living in Ontario between 2010 and 2012 was created from linked databases and classified into eight world regions of origin. Appropriate screening rates were calculated for each region by age group and selected sociodemographic, immigration, and healthcare-related characteristics. The association between appropriate screening across the eight regions of origin and selected sociodemographic, immigration, and health-related characteristics was explored using multivariate Poisson regression. Screening varied by region of origin, with South Asian women (48.5%) having the lowest and Caribbean and Latin American women (63.7%) the highest cancer screening rates. Factors significantly associated with lower screening across the world regions of origin included living in the lowest income neighborhoods, having a refugee status, being a new immigrant, not having a regular physical examination, not being enrolled in a primary care patient enrollment model, having a male physician, and having an internationally trained physician. Multiple interventions entailing cross-sector collaboration, promotion of patient enrollment models, community engagement, comprehensive and intensive outreach to women, and knowledge translation and transfer to physicians should be considered to address screening disparities among immigrant population. Consideration should be given to design and delivery of culturally appropriate and easily accessible cancer screening programs targeted at high- risk immigrant subgroups, such as women of South Asian origin, refugees, and new immigrants.

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)0.001
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 score0.022
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.048
GPT teacher head0.308
Teacher spread0.260 · 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

Citations100
Published2016
Admission routes3
Has abstractyes

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