Theory‐based communication skills training for medicine counter assistants to improve consultations for non‐prescription medicines
Bibliographic record
Abstract
CONTEXT: Medicine counter assistants (MCAs) supply the majority of non-prescription medicines (NPMs) to consumers. Suboptimal communication during consultations between consumers and MCAs has been identified as a major cause of inappropriate supply. Evidence from medical consultations suggests that training in specified communication skills can change professional behaviour. METHODS: A feasibility study was conducted to evaluate the effect of theory-based communication skills training for MCAs. Thirty MCAs were recruited from 21 community pharmacies in Grampian, Scotland. The intervention comprised 2 4-hour training sessions, held 1 month apart. The sessions were informed by results from previous studies and the Calgary-Cambridge evidence-based model of communication skills training. Strategies for guiding individuals through change were adopted from cognitive behavioural therapy techniques. The theory of planned behaviour was used to assess potential pathways to behaviour change. Recorded data were collected during covert visits to the pharmacies by simulated patients at baseline and 1 month after each training session. Communication performance was measured as the number and type of questions asked. RESULTS: Compared with baseline measures, the total number of questions asked increased in the intervention group at both time-points. No change was shown in the control group between baseline and follow-up 1, and a decrease was shown in the total number of questions from follow-up 1 to 2. The intervention appeared to have greater effect on consultations involving advice, compared with those concerning product requests. DISCUSSION: Communication performance improved following training. Increased information exchange is associated with guideline-compliant supply of NPMs. A substantive randomised, controlled trial is now planned to assess the intervention.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".