Deconstructing cancer: what makes a good‐quality news story?
Bibliographic record
Abstract
OBJECTIVE: To describe an in-depth analysis of the content and quality of stories about new cancer interventions in Australian media. DESIGN AND SETTING: Search of the Media Doctor Australia media-monitoring website for stories about newly reported cancer interventions, including drugs, diagnostic tests, surgery and complementary therapies, that had been collected from June 2004 to June 2009 and rated for quality using a validated rating instrument. A mixed-methods approach was used to analyse data and story content. Data from the website on stories about other new health interventions and procedures were compared. MAIN OUTCOME MEASURES: Differences in quality scores between cancer-related news stories ("cancer stories") and other stories, and between types of media outlet; differences in how cancer was reported in terms of cancer type, morbidity, mortality, and in the use of hyperbole and emotive language. RESULTS: 272 unique cancer stories were critically reviewed by Media Doctor Australia. Cancer stories had significantly higher scores for quality than other stories (F=7.1; df=1; P=0.008). Most cancer stories concerned disease affecting the breast or prostate gland, with breast cancer appearing to be over-represented as a topic relative to its incidence. Pairwise comparisons showed statistically significant superiority for broadsheet newspaper stories over online stories (F=12.7; df=1; P<0.001) and television stories (F=10.7; df=1; P=0.001). Descriptions of morbidity and mortality were variable and often confusing in terms of numbers, time periods and locations. Literary devices including hyperbole and emotive language were used extensively, mostly by the researchers. CONCLUSIONS: While reporting of cancer in the general media is of low quality, many of the poorer aspects of content are directly attributable to the researchers. Researchers and journals need to do more to ensure that a higher standard of information about cancer is presented to the media.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".