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Record W1969456350 · doi:10.1136/bmj.d8164

Influence of medical journal press releases on the quality of associated newspaper coverage: retrospective cohort study

2012· article· en· W1969456350 on OpenAlexaff
Lisa M. Schwartz, Steven Woloshin, Alice O. Andrews, Thérèse A. Stukel

Bibliographic record

VenueBMJ · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of Toronto
FundersNational Cancer Institute
KeywordsNewspaperMedicineLexisFamily medicineMedia studiesSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether the quality of press releases issued by medical journals can influence the quality of associated newspaper stories. DESIGN: Retrospective cohort study of medical journal press releases and associated news stories. SETTING: We reviewed consecutive issues (going backwards from January 2009) of five major medical journals (Annals of Internal Medicine, BMJ, Journal of the National Cancer Institute, JAMA, and New England Journal of Medicine) to identify the first 100 original research articles with quantifiable outcomes and that had generated any newspaper coverage (unique stories ≥100 words long). We identified 759 associated newspaper stories using Lexis Nexis and Factiva searches, and 68 journal press releases using Eurekalert and journal website searches. Two independent research assistants assessed the quality of journal articles, press releases, and a stratified random sample of associated newspaper stories (n=343) by using a structured coding scheme for the presence of specific quality measures: basic study facts, quantification of the main result, harms, and limitations. MAIN OUTCOME: Proportion of newspaper stories with specific quality measures (adjusted for whether the quality measure was present in the journal article's abstract or editor note). RESULTS: We recorded a median of three newspaper stories per journal article (range 1-72). Of 343 stories analysed, 71% reported on articles for which medical journals had issued press releases. 9% of stories quantified the main result with absolute risks when this information was not in the press release, 53% did so when it was in the press release (relative risk 6.0, 95% confidence interval 2.3 to 15.4), and 20% when no press release was issued (2.2, 0.83 to 6.1). 133 (39%) stories reported on research describing beneficial interventions. 24% mentioned harms (or specifically declared no harms) when harms were not mentioned in the press release, 68% when mentioned in the press release (2.8, 1.1 to 7.4), and 36% when no press release was issued (1.5, 0.49 to 4.4). 256 (75%) stories reported on research with important limitations. 16% reported any limitations when limitations were not mentioned in the press release, 48% when mentioned in the press release (3.0, 1.5 to 6.2), and 21% if no press release was issued (1.3, 0.50 to 3.6). CONCLUSION: High quality press releases issued by medical journals seem to make the quality of associated newspaper stories better, whereas low quality press releases might make them worse.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.040
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.363
GPT teacher head0.512
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainReporting
GenreEmpirical

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".

Quick stats

Citations138
Published2012
Admission routes1
Has abstractyes

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