30+ years of media analysis of relevance to chronic disease: a scoping review
Bibliographic record
Abstract
BACKGROUND: Chronic, non-communicable diseases are a significant public health priority, requiring action at individual, community and population levels, and public and political will for such action. Exposure to media, including news, entertainment, and advertising media, is likely to influence both individual behaviours, and attitudes towards preventive actions at the population level. In recent years there has been a proliferation of research exploring how chronic diseases and their risk factors are portrayed across various forms of media. This scoping review aims to map the literature in this area to identify key themes, gaps, and opportunities for future research in this area. METHODS: We searched three databases (Medline, PsycINFO and Global Health) in July 2016 and identified 499 original research articles meeting inclusion criteria: original research article, published in English, focusing on media representations of chronic disease (including how issues are framed in media, impact or effect of media representations, and factors that influence media representations). We extracted key data from included articles and examined the health topics, media channels and methods of included studies, and synthesised key themes across studies. RESULTS: Our findings show that research on media portrayals of chronic disease increased substantially between 1985 and 2016. Smoking and nutrition were the most frequent health topics, and television and print were the most common forms of media examined, although, as expected, research on online and social media channels has increased in recent years. The majority of studies focused on the amount and type of media coverage, including how issues are framed, typically using content analysis approaches. In comparison, there was much less research on the influences on and consequences of media coverage related to chronic disease, suggesting an important direction for future work. CONCLUSIONS: The results highlight key themes across media research of relevance to chronic disease. More in-depth syntheses of studies within the identified themes will allow us to draw out the key patterns and learnings across the literature.
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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 | 0.017 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.037 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".