Climate for evidence-informed health systems: A print media analysis in 44 low- and middle-income countries that host knowledge-translation platforms
Bibliographic record
Abstract
BACKGROUND: We conducted a print media analysis in 44 countries in Africa, the Americas, Asia, and the Eastern Mediterranean in order to understand one dimension of the climate for evidence-informed health systems and to provide a baseline for an evaluation of knowledge-translation platforms. Our focus was whether and how policymakers, stakeholders, and researchers talk in the media about three topics: policy priorities in the health sector, health research evidence, and policy dialogues regarding health issues. METHODS: We developed a search strategy consisting of three progressively more delimited phases. For each jurisdiction, we searched Major World Publications in LexisNexis Academic News for articles published in 2007, selected relevant articles using one set of general criteria and three sets of concept-specific criteria, and coded the selected articles to identify common themes. Second raters took part in the analysis of Lebanon and Malaysia to assess inter-rater reliability for article selection and coding. RESULTS: We identified approximately 5.5 and 5 times more articles describing health research evidence compared to the number of articles describing policy priorities and policy dialogues, respectively. Few articles describing health research evidence discussed systematic reviews (2%) or health systems research (2%), and few of the policy dialogue articles discussed researcher involvement (9%). News coverage of these concepts was highly concentrated in several countries like China and Uganda, while few articles were found for many other jurisdictions. Kappa scores were acceptable and consistently greater than 0.60. CONCLUSIONS: In many countries the print media, at least as captured in a global database, are largely silent about three topics central to evidence-informed health systems. These findings suggest the need for proactive-media engagement strategies.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".