Portrayal of Genetic Risk for Breast Cancer in Ethnic and Non-Ethnic Newspapers
Bibliographic record
Abstract
There has been enormous attention paid to the genetics of breast cancer in this era of genomic medicine. A great deal of the interest has been generated through discourse in the public mass media. However, genetic risk is a probabilistic concept and one that requires adequate numeracy skills. The purpose of this qualitative content analysis was to describe and evaluate the portrayal of genetic risk for breast cancer in mass print media. Mass print newspapers targeting high (Ashkenazi Jews) and low (general Canadian population) genetic risk audiences and published at least monthly, available in English and accessible through public archives at the National Library of Canada, were identified and hand searched for articles on breast cancer. Approximately 47% of breast cancer articles in 6 Jewish newspapers and published between 1996-2000 identified genetics in the title, first or last paragraph compared with 17% of 145 articles in 6 provincial newspapers published in 2000. The description of breast cancer risk was equally problematic in print media targeting high and low risk audiences. Statistics were presented in complex and contradictory ways, with, for example, the confounding of individual and population based risk estimates. Inconsistent messages about the value of genetic screening for breast cancer characterized articles in both ethnic and non-ethnic newspapers. Deciphering the information into a comprehensible form is likely challenging, particularly in light of widespread numeric-literacy limitations. The publication of discrepant research findings and the perplexing statistical information consequently brought into question the credibility of the scientific process and the recommendations of health care professionals.
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 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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".