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Record W2408922446

Disparities in the coverage of cancer information in ethnic minority and mainstream mass print media.

2005· article· en· W2408922446 on OpenAlexaffabout
Laurie Hoffman‐Goetz, Daniela B. Friedman

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEthnic groupReadabilityMainstreamCancerMedicineNewspaperLung cancerDemographyOncologyInternal medicinePolitical scienceSociologyMedia studiesComputer science
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Significant disparities in cancer mortality exist as a function of ethnicity and race in North America. Little is known, however, about the presentation of cancer information in mass media that targets ethnic minority groups. OBJECTIVES: 1) To evaluate the volume and type of cancer coverage and the readability of cancer articles in Canadian mainstream and ethnic minority newspapers; and 2) to compare newspaper coverage of cancer with Canadian cancer mortality. DESIGN: Seven mainstream and 25 ethnic minority (Jewish, First Nations, Black/Caribbean, East Indian) English-language newspapers were assessed for cancer coverage in the year 2000. Articles were analyzed by using frequencies and nonparametric tests. The total number of cancer articles (N=171) in ethnic minority papers and a random 20% from mainstream papers were also evaluated for readability level by using SMOG. RESULTS: There were a total of 748 cancer articles (721 mainstream; 27 ethnic). Coverage was weighted towards breast cancer (20.1% mainstream, 33.3% ethnic of cancer articles) and contained little or no coverage of prostate (7.4% mainstream, 8.6% ethnic), colorectal (3.9% mainstream, 3.7% ethnic), or lung (3.9% mainstream, 0 ethnic) cancers. The mean SMOG readability scores were Grades 12.7 and 13.2 for mainstream and ethnic papers, respectively. Readability scores differed significantly in ethnic newspapers, with the most difficult (highest readability) levels in East Indian (Grade 16.3) and the easiest (lowest readability) levels in First Nations (Grade 11.3) papers. Cancer articles were not highly culturally tailored, as measured by identification of specific ethnic minority groups within ethnic and mainstream newspapers. CONCLUSIONS: Cancer coverage in ethnic and mainstream newspapers did not accurately reflect the leading causes of cancer death in Canada. Results also suggest the need for the collection of cancer data by ethnic minority group in Canada. Without the disaggregation of cancer statistics by ethnicity, we cannot inform high-risk subgroups of the population and appropriately tailor cancer prevention and treatment programs.

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)0.003
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 score0.308
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.056
GPT teacher head0.390
Teacher spread0.333 · 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

Citations43
Published2005
Admission routes2
Has abstractyes

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