What can we learn from the public’s understanding of drug information and safety? A population survey
Bibliographic record
Abstract
OBJECTIVE: The aim of our study was to analyse the perceptions of the public on medicine information and safety and on consumer reporting of suspected adverse drug reactions (ADR). METHODS: A voluntary survey was conducted in a population ≥18 years of age in Asturias, a region in northern Spain. The survey was designed to be completed in a face-to-face street interview or completed independently by the public. The survey consisted of structured questions organised in four sections: (1) demographic data, (2) use of medicines, (3) reading and understanding of the patient information leaflet (PIL) and (4) awareness and perception about consumer reporting of ADR. KEY FINDINGS: A total of 402 surveys were given and analysed; 295 were completed independently and 107 were completed in street interviews. Of the population surveyed, 82.3% had taken some drug(s) in the previous 3 months, although only 62.4% had performed so by medical prescription. A quarter of respondents claimed that they never read the PIL of medicines, 12.7% that they sometimes read it, and 61.4% that they always read this information. A high percentage (82.8%) of respondents reported that they were not aware of consumer reporting of ADR, and 86.1% stated their agreement with this option. CONCLUSIONS: The public has great interest in useful information about all aspects involved in the use of medicines. This includes consumer reporting of suspected ADR, which is still unknown to many people.
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".