Quality of information in news media reports about the effects of health interventions: Systematic review and meta-analyses
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background</ns3:bold> </ns3:p> <ns3:p>Many studies have assessed the quality of news reports about the effects of health interventions, but there has been no systematic review of such studies or meta-analysis of their results. We aimed to fill this gap (PROSPERO ID: CRD42018095032).</ns3:p> <ns3:p> <ns3:bold>Methods</ns3:bold> </ns3:p> <ns3:p>We included studies that used at least one explicit, prespecified and generic criterion to assess the quality of news reports in print, broadcast, or online news media, and specified the sampling frame, and the selection criteria and technique. We assessed criteria individually for inclusion in the meta-analyses, excluding ineligible criteria and criteria with inadequately reported results. We mapped and grouped criteria to facilitate evidence synthesis. Where possible, we extracted the proportion of news reports meeting the included criterion. We performed meta-analyses using a random effects model to estimate such proportions for individual criteria and some criteria groups, and to characterise heterogeneity across studies. </ns3:p> <ns3:p> <ns3:bold>Results</ns3:bold> </ns3:p> <ns3:p>We included 44 primary studies in the review, and 18 studies and 108 quality criteria in the meta-analyses. Many news reports gave an unbalanced and oversimplified picture of the potential consequences of interventions. A limited number mention or adequately address conflicts of interest (22%; 95% CI 7%-49%) (low certainty), alternative interventions (36%; 95% CI 26%-47%) (moderate certainty), potential harms (40%; 95% CI 23%-61%) (low certainty), or costs (18%; 95% CI 12%-28%) (moderate certainty), or quantify effects (53%; 95% CI 36%-69%) (low certainty) or report absolute effects (17%; 95% CI 4%-49%) (low certainty). </ns3:p> <ns3:p> <ns3:bold>Discussion</ns3:bold> </ns3:p> <ns3:p>There is room for improving health news, but it is logically more important to improve the public’s ability to critically appraise health information and make judgements for themselves.</ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".