Assessment of Cultural Sensitivity of Cancer Information in Ethnic Print Media
Bibliographic record
Abstract
Ethnic minority populations prefer cancer information that is respectful of their customs and beliefs about health and illness. Community newspapers are an important source of cancer information for ethnic groups. Our purpose is to evaluate the cultural sensitivity of cancer information in mass print media targeting ethnic minority readership. We assessed for cultural sensitivity 27 cancer articles published in English-language ethnic newspapers (Jewish, First Nations, Black/Caribbean, East Indian) in 2000 using the Cultural Sensitivity Assessment Tool (CSAT). We found that the overall average CSAT score of 27 cancer articles was 2.71. (Scores<2.50 were classified as culturally insensitive.) Articles in First Nations newspapers were more culturally sensitive according to the CSAT (X=2.86), followed by articles in Black/Caribbean (X=2.79) and Jewish (X=2.78) papers. Cancer articles from East Indian newspapers had a mean CSAT score of 2.30 and were classified as culturally insensitive. Four articles were considered culturally sensitive but did not mention ethnic populations as intended readers or as high-risk groups for cancer. We found that, using the CSAT measure, overall, cancer articles in ethnic newspapers included in this study were culturally sensitive. Given limitations of this instrument, we recommend an additional checklist for evaluating the cultural sensitivity of printed cancer information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".