Delphi survey of experts’ opinions on strategies used by community pharmacists to reduce over‐the‐counter drug misuse
Bibliographic record
Abstract
AIM: To explore the views of experts within the fields of pharmacy and addiction on the value of current strategies and possible alternatives and to reach an agreement on best practice in the sale of over-the-counter (OTC) medicines which are liable to misuse. DESIGN: Using a modified Delphi approach, an anonymous, international, three-stage, postal questionnaire was conducted that generated both qualitative and quantitative data. PARTICIPANTS: Of those contacted by telephone (164) from the United Kingdom, Australia, Canada, New Zealand and United States, 109 experts (66%) agreed to take part. Forty-three per cent (47/109) completed all three stages of the study. MEASUREMENTS: A Delphi technique was employed to gather data. The second and final questionnaires were constructed from the responses to the preceding questionnaires. Content analysis of the qualitative data was carried out at each stage. Statistical analyses of the influence of demographic factors, degree of shift in overall opinion between the first and second stages and degree of agreement between respondents at each stage were also conducted. FINDINGS: A consensus was reached on the strategies considered the most important and effective. Key areas include improving access to current information, improved staff training, addressing the issues of non-pharmacy outlets and Internet pharmacy sites. Concerns were expressed regarding the possible conflict between commercial and customer interests. CONCLUSIONS: The consensus view presented offers practical and realistic guidance for policy-makers and community pharmacists on the sale of OTC products. It reflects the best evidence to date of expert views in this area and accords with current UK guidelines. The effective implementation of these strategies can only be achieved with improved communication and coordination at local and national level.
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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 | 0.042 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".