The Use of Traditional Media for Public Communication about Medicines: A Systematic Review of Characteristics and Outcomes
Bibliographic record
Abstract
A systematic review was conducted to identify, appraise, and synthesize data from original research investigating the use of traditional media for public communication about medicines. Databases were searched for studies conducting quantitative or qualitative analyses between the years 2007 and 2017. Data extraction and assessment of the quality of the resulting studies was conducted by one reviewer and checked for accuracy by a second reviewer. A total of 57 studies met the inclusion criteria. Studies were grouped as follows: "newspapers and other print media" (n = 42), "television" (n = 9), and "radio and a combination of media" (n = 6). Content analysis (n = 34) was the most frequent research design, followed by surveys or interviews (n = 14) and randomized controlled trials (RCTs) (n = 9). Advertising, public awareness, and health administration were the most common themes, and the medicines most analyzed were vaccines, particularly human papillomavirus (HPV) and influenza. Studies conducted in the United States were the most frequent, followed by other high-income countries such as Canada and the United Kingdom. The lack of consistent studies of the effects of media campaigns stresses the importance of the use of standardized research methodologies. Theoretical and practical implications of the findings for further research are discussed.
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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 | 0.029 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".