MétaCan
Menu
Back to cohort
Record W1979414801 · doi:10.1080/13557850500120751

Ethnicity, Genetics, and Breast Cancer: Media Portrayal of Disease Identities

2005· article· en· W1979414801 on OpenAlexafffundabout
Lorie Donelle, Laurie Hoffman‐Goetz, Juanne N. Clarke

Bibliographic record

VenueEthnicity and Health · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity of Waterloo
FundersNational Cancer InstituteSocial Sciences and Humanities Research Council of Canada
KeywordsBreast cancerNewspaperEthnic groupPopulationMedicineDiseaseRisk factorCancerDemographyEnvironmental healthPolitical scienceSociologyInternal medicineLaw

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe, compare, and analyze how the risk of breast cancer is framed in newspapers directed towards an ethnic minority population (Jewish) with higher risk of inherited breast cancer compared with newspaper coverage for the general population (Anglo-Canadian) without this risk. DESIGN: This investigation utilized a mixed methods (quantitative and qualitative) approach. The design emphasized a content analysis conducted on ethnically specific and non-ethnic newspaper articles. RESULTS: It is noteworthy that the 'Jewish' newspapers devote a substantially larger proportion of articles on breast cancer to genetic risk as the key risk factor for this disease. Articles in the Jewish newspapers tend to link being a Jewish woman with being at risk for a diagnosis of breast cancer. This ethnic 'identity' is reinforced through the repeated association of Jewish heritage and genetic breast cancer risk at the exclusion of other known risk factors. This isolated genetic link to breast cancer is not a message that is replicated within the provincial newsprint articles. CONCLUSIONS: These findings assist in the facilitation of prevention and treatment of those with or at risk of breast cancer. The health policy implications of this portrayal as well as suggestions for change are considered.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.439

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 studies0.0000.000
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 head0.029
GPT teacher head0.340
Teacher spread0.311 · 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

Citations26
Published2005
Admission routes3
Has abstractyes

Explore more

Same venueEthnicity and HealthSame topicBRCA gene mutations in cancerFrench-language works237,207