Conflicts of interest in the reporting of biomedical research in mainstream newspapers in Canada
Bibliographic record
Abstract
Ethical behaviour by investigators is the cornerstone of scientific research. Recognizing, declaring and avoiding a conflict of interest are key responsibilities for biomedical researchers, particularly since commercial enterprises, such as pharmaceutical companies, have become major funding sources of research. Proactive disclosure of researchers' financial relationships is now a requirement for publication in most scientific journals. The question that arises is whether this same increased scrutiny of financial disclosure and potential for conflict of interest has extended to the mainstream press in Canada. A content analysis of biomedical research articles that appeared in Canadian daily newspapers from 2001 to 2008 showed that 82 per cent of the articles failed to identify the financial connection that existed between the researcher(s) and the commercial funder, and nearly half of the articles did not even identify the commercial funding source of the research. A text analysis showed that 94 per cent of the articles were positive about the drug/device cited by the research, and positive, optimistic words such as “breakthrough”, “significant”, “hope” and “promising” were often used in the news articles. Reporters frequently frame biomedical research articles using a battle-like template that describes a fight between good and evil. Another common approach was to frame the article as a message of hope for the future. A genre analysis showed that the genre of medical research news articles published in newspapers is highly dissimilar to the genre of medical research articles published in scientific journals. It is likely these two genres have been constructed to appeal to very different target audiences. The study results show overwhelmingly that readers are not provided with key information about potential financial conflicts of interest involving the researchers and the commercial sources of funding for the research. Such lack of transparency thwarts the reader’s ability to reach informed conclusions about whether or not the research has been either explicitly or implicitly influenced by the researcher’s potential conflict.
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 | 0.015 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.022 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".