Bibliographic record
Abstract
BACKGROUND: UK Government policy increasingly encourages self-care of minor illnesses, including self-medication. Analgesics constitute a quarter of UK over-the-counter medicines sales, but concerns have been expressed about their potential for inappropriate use. OBJECTIVES: To estimate the prevalence of recent use of non-prescription analgesics in Scotland, to describe by whom they are used, and to estimate inappropriate use. METHOD: A cross-sectional postal survey consisting of a self-completed questionnaire that collected data on respondents' use of non-prescription and prescription medicines, as well as demographic and lifestyle data. The sample comprised 2708 subjects of 18 years and over, randomly selected from the Scottish electoral roll. RESULTS: The response rate was 55% (n=1501). Some 37% (555/1501) of respondents had used a non-prescription analgesic in the previous two weeks. Analgesics accounted for 59% (636/1081) of all non-prescription medicines used in that period. After controlling for all other variables, age, sex, level of education, self-reported health status, prescription exemption status, and use of prescription analgesics, remained significant predictors of non-prescription analgesic use. There was evidence of possible inappropriate use of non-prescription analgesics including use of multiple analgesics (n=67), use by individuals self-reporting conditions associated with cautious use of certain analgesics (n=51), and potential drug-drug interactions (n=15). A few respondents appeared to be using non-prescription analgesics to supplement medical treatment of chronic conditions (n=4). CONCLUSIONS: Our findings have demonstrated a high level of use of non-prescription analgesics amongst the general public, with significant potential for inappropriate use. As we move towards a culture of increased self-management of minor illness, this demonstrated need for improved pharmacovigilance of non-prescribed medicines must be addressed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".