Pharmacological articles in the German magazine DIE ZEIT (THE TIME)—content, adequacy, and comprehensibility
Bibliographic record
Abstract
The German weekly magazine DIE ZEIT (THE TIME) reaches more than one million readers per issue, mainly from high-income social classes. Pharmacological content is frequent in DIE ZEIT. As it therefore reaches many people who generally have no in-depth knowledge of pharmacology, it can be assumed that DIE ZEIT is an important primary source of information. It should be its task to depict the drugs widely used by the population and to present them correctly and comprehensibly. This study analyzes 71 articles from 2012 to 2022 in terms of form, content, and comprehensibility. The analysis shows that in DIE ZEIT, drug groups largely correspond to the prescription figures and disease prevalence in Germany, with cardiovascular, neuropsychiatric, and pain medications being frequently discussed. There are deviations in the case of oncological drugs, for example, which are discussed more frequently than prescribed. New drug approvals are reported less frequently, and when they are, it is usually about the research phase. DIE ZEIT often reports on findings that are less than a week old and frequently quotes trustworthy experts, but no scientific sources can be found in around a quarter of the articles. A COVID-19 effect can also be identified in the years 2020 to 2022, as reporting on drugs for the treatment of coronavirus disease 2019 (COVID-19) predominated. An important point of criticism was identified with regard to comprehensibility. Less than half of the articles achieved the cut-off value for general comprehensibility specified by the Textlab analysis program, and only one article achieved the value for target group-oriented comprehensibility. This analysis confirms the problem that science communication is often too complicated and incomprehensible. It discusses the tension between the prescribed drugs and the mission of DIE ZEIT to entertain and should serve as a basis for analyzing other newspapers. Finally, we make specific suggestions how presentation of pharmacological topics in lay media can be improved in the future.
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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 | 0.022 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".