Making headlines: an analysis of US government-funded cancer research mentioned in online media
Bibliographic record
Abstract
OBJECTIVE: To characterise how online media coverage of journal articles on cancer funded by the US government varies by cancer type and stage of the cancer control continuum and to compare the disease prevalence rates with the amount of funded research published for each cancer type and with the amount of media attention each receives. DESIGN: A cross-sectional study. SETTING: The United States. PARTICIPANTS: The subject of analysis was 11 436 journal articles on cancer funded by the US government published in 2016. These articles were identified via PubMed and characterised as receiving online media attention based on data provided by Altmetric. RESULTS: 16.8% (n=1925) of articles published on US government-funded research were covered in the media. Published journal articles addressed all common cancers. Frequency of journal articles differed substantially across the common cancers, with breast cancer (n=1284), lung cancer (n=630) and prostate cancer (n=586) being the subject of the most journal articles. Roughly one-fifth to one-fourth of journal articles within each cancer category received online media attention. Media mentions were disproportionate to actual burden of each cancer type (ie, incidence and mortality), with breast cancer articles receiving the most media mentions. Scientific articles also covered the stages of the cancer continuum to varying degrees. Across the 13 most common cancer types, 4.4% (n=206) of articles focused on prevention and control, 11.7% (n=550) on diagnosis and 10.7% (n=502) on therapy. CONCLUSIONS: Findings revealed a mismatch between prevalent cancers and cancers highlighted in online media. Further, journal articles on cancer control and prevention received less media attention than other cancer continuum stages. Media mentions were not proportional to actual public cancer burden nor volume of scientific publications in each cancer category. Results highlight a need for continued research on the role of media, especially online media, in research dissemination.
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 | 0.006 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".