News Coverage of Colorectal Cancer on Google News: Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) is one of the leading causes of cancer death in the United States. The incidence and prevalence of CRC have historically increased with age. Although rates of CRC in the United States have been decreasing over the past decades among those aged ≥65 years, there has been an uptick among those in younger age brackets. Google News is one of the biggest traffic drivers to top news sites. It aggregates and shares news highlights from multiple sources worldwide and organizes them by content type. Despite the widespread use of Google News, research is lacking on the type of CRC content represented in this news source. OBJECTIVE: The purpose of this study was to analyze content related to CRC screening and prevention in Google News articles published during National Colorectal Cancer Awareness Month (March 2022). METHODS: Data collection for this cross-sectional study was conducted in March 2022-National Colorectal Cancer Awareness Month. Using the term colorectal cancer, 100 English-language Google News articles were extracted and coded for content. A combined approach-deductive and inductive coding-was utilized. Descriptive analyses were conducted, and frequency distributions were reported. Univariable analyses were performed to assess differences between articles that mentioned CRC screening and those that did not via chi-square tests. RESULTS: Of the 100 articles reviewed, nearly half (n=49, 49%) were created by health news organizations, and another 27% (n=27) were created by television news services. The predominant themes in the content included age at the onset of disease (n=59, 59%), mortality related to CRC (n=57, 57%), and the severity of disease (n=50, 50%). Only 18% (n=18) of articles discussed CRC disparities, 23% (n=23) mentioned that there are hereditary forms of the disease, 36% (n=36) spoke of colonoscopy to screen for the disease, and 37% (n=37) mentioned how the disease is treated. Although most articles mentioned CRC screening (n=61, 61%), it was striking that sex was only mentioned in 34% (21/61) of these articles, colonoscopy was mentioned in 46% (28/61), and diet was mentioned in 30% (18/61). CONCLUSIONS: Heightening the public's awareness of this disease is important, but it is critical that messages related to how preventable this cancer is, who is the most likely to develop CRC, and what can be done to detect it in the early stages when the disease is the most curable be the critical elements of dialogue, particularly during National Colorectal Cancer Awareness Month. There is a need to disseminate information about early-onset CRC and the importance of screening, especially among populations with low rates of uptake. Web-based news is potentially an underutilized communication mechanism for promoting CRC screenings as secondary prevention measures for high-risk groups.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".