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Record W7017516177

Breast Cancer Screening in Racialized Women: Implications for Health Equity

2014· report· en· W7017516177 on OpenAlexaboutno aff

Bibliographic record

VenueIssue Lab (Candid) · 2014
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRacializationBreast cancer screeningBreast cancerEthnic groupCancer screeningHealth equitySocioeconomic statusHealth care
DOInot available

Abstract

fetched live from OpenAlex

Inequalities across the breast cancer continuum due to racialization have significantly affected women's access to screening programs, diagnosis, treatment and survival. To ensure there is equitable access to quality care there needs to be a better understanding of broader systemic issues. Despite the existence of breast cancer prevention strategies across Canada, inequitable access to screening has barred many women from receiving adequate medical attention. More than half of recent immigrants (those who have been in Canada for less than 10 years) who are eligible for screening did not utilize the program in the previous two years compared to 26 percent of Canadian-born women. Currently, prevention through screening is the primary form of breast cancer control in Canada, thus the differential access to screening among social, geographic, demographic and racial groups can severely affect one's chances of surviving. This paper provides an overview of inequitable outcomes across the breast cancer continuum due to racialization, with a particular focus on screening. It provides a brief description of racial and ethnic differences in screening utilization, diagnosis and survival drawing on local, national, and international data. It also provides a summary of important barriers to screening in racialized and ethnic minority women. It concludes with implications for Ontario and the Greater TorontoArea (GTA), and identifies possible directions forward.Racial, ethnic and socioeconomic differences in breast cancer screening have an important impact on the chance of both developing and dying from breast cancer. Therefore, we must acknowledge the negative impact of racialization and racism on health outcomes in Canada. In order to adequately address this problem, there is a need for community-based research that allows us to gain better insight into the perceptions, lived experiences and the multiple and often competing needs of women across racialized and immigrant communities in Ontario.

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.427
Threshold uncertainty score0.849

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.084
GPT teacher head0.450
Teacher spread0.366 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2014
Admission routes1
Has abstractyes

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