Medicine and media: The role of journalism in public health education
Bibliographic record
Abstract
6090 Background: Public dissemination of medical information via the media requires journalists' research on the subject matter and their interactions with doctors or scientists. About 70% of cancer patients search the media for information, and one-third feels that their decisions are influenced by this information (Chen and Siu, J Clin Oncol, 2001). This study aimed to understand the media's perception of its role in the dissemination of medical information. Methods: Twenty-five Canadian medical journalists from various media sources (newspaper (NP), magazine (M), radio (R) and television (TV)) were contacted. A structured telephone interview was conducted to evaluate research strategy and perception of role. Responses were analyzed qualitatively. Results: The response rate was 64% (16/25): 5 NP, 4 M, 1 R and 6 TV. Most (13/16) did not have prior health-related education. In identifying a news story, NP considered the potential to change the standard of care the most important element, whereas for TV existent public interest was the most important. Most used medical journals to identify a news story, and utilized sources including expert opinions, authors of original studies, journals, internet and textbooks for research. Internet was consistently used for background research. Most (9/16) perceived the primary role of the media as initiating public interest instead of providing comprehensive health information. Most felt that the media equipped patients to ask questions, to challenge authority and to improve communications with doctors. All but one felt that the media had a positive impact on the doctor-patient relationship. Some (6/16) commented that doctors could not simplify their language. Doctors are felt to be hesitant about communicating with the media. Timely responses from doctors and an organized forum for doctors and journalists to interact were suggested as possible ways to assist the media in public education. Conclusions: The media perceived the lack of proper communication between doctors and journalists as a barrier to the public dissemination of medical information. Future interventions should aim to improve such communication and improve public health education. No significant financial relationships to disclose.
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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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".