doi:10.1093/fampra/cmh719 Family Practice Advance Access originally published online on 7 January 2005 How and why are non-prescription analgesics used in Scotland?
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
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.736 | 0.231 |
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".