Disparities in the coverage of cancer information in ethnic minority and mainstream mass print media.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".