Journalism as Health Education: Media Coverage of a Nonbranded Pharma Web Site
Bibliographic record
Abstract
OBJECTIVE: As healthcare consumers increasingly use the Internet as a source for health information, direct-to-consumer (DTC) prescription drug advertising online merits additional attention. The purpose of this research was to investigate media coverage of the joint marketing program linking the movie Happy Feet and the nonbranded disease education Web site FluFacts-a resource from Tamiflu flu treatment manufacturer Roche Laboratories Inc. MATERIALS AND METHODS: Twenty-nine articles (n = 29) were found covering the Happy Feet-FluFacts marketing campaign. A coding guide was developed to assess elements of the articles, including those common in the sample and information that ideally would be included in these articles. Two coders independently coded the articles, achieving intercoder agreement of κ = 0.98 before resolving disagreements to arrive at a final dataset. RESULTS: The majority of articles reported that Roche operated FluFacts (51.7%) and mentioned the product Tamiflu (58.6%). Almost half (48.3%) reported FluFacts was an educational resource; yet, no articles mentioned other antiviral medications or nonmedical options for preventing the flu. Almost a quarter of the articles (24.1%) provided a call to action-telling readers to visit FluFacts or providing a link for them to do so. CONCLUSIONS: Findings suggest that journalists' coverage of this novel campaign-likely one of the goals of the campaign-helped spread the message of the Happy Feet-FluFacts relationship, often omitting other useful health information. Additional research is needed to better understand online DTC campaigns and how consumers react to these campaigns and resulting media coverage and to inform the policymakers' decisions regarding DTC advertising online.
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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 | 0.010 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".