Pediatric Medication Safety and the Media: What Does the Public See?
Bibliographic record
Abstract
BACKGROUND: In the safety community, it is widely thought that a culture of safety is required to achieve high levels of safety. However, the press tends to report accidents, which are negative by their nature. Pediatric cases are often especially tragic. Relatively few data have been available on the role that the media play in forming opinions about patient safety and the subsequent impact on the culture of safety. METHODS: To address these issues, we analyzed newspaper coverage of pediatric medication errors and adverse drug events. We searched Lexis Nexis for newspaper articles on pediatric medication safety from the United States, Canada, United Kingdom, Australia, and Ireland, during a 10-year period (1994-2004), by using specific keywords. Main outcome measures were the number of articles (adjusted for population), the type of events covered, and article slant. We also examined qualitatively the overall themes and the extent to which these articles portrayed a culture of safety to the public. RESULTS: Throughout the world, there was a steady increase in articles on pediatric medication safety, peaking in 2003, with the highest per-capita rate in Canada. Approximately 65% of articles were about patient incidents, 20% mentioned policy, and 25% discussed research. Of the reported events judged to be negative for patient safety, 75% were covered in a neutral manner and 19% were covered in an unduly negative manner. CONCLUSIONS: Media coverage of pediatric medication safety has increased in the past 10 years. Reporting of patient safety failures was generally fair, and reports were generally framed in light of a culture of safety.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".