Making medication communication visible in community pharmacies-pharmacists' experience using a question prompt list in the patient meeting
Bibliographic record
Abstract
BACKGROUND: Even though patient engagement in the pharmacy encounter is low, few studies focus on activating patients. A Question Prompt List (QPL) has been used successfully in other parts of healthcare to encourage patients to raise their questions and concerns. For a QPL to be useful in a pharmacy setting, it first must be considered valuable and be accepted by pharmacists. OBJECTIVE: To investigate the experience of community pharmacists using a QPL in counseling patients about prescribed medications. METHODS: An explorative, qualitative study was conducted in 2020. A QPL, for use in pharmacy counseling, was developed based on previous literature. Semi-structured interviews were held with pharmacists. A thematic analysis approach was conducted, and the analytical framework Technology Acceptance Model (TAM) was used. RESULTS: Data were collected in 7 Swedish community pharmacies in interviews with 29 purposefully selected pharmacists. Three themes were identified: Perceived usefulness: the impact of the QPL on patient activation in the encounter, Perceived ease of use of the QPL in pharmacies, and Increasing the perceived usefulness and ease of use of the QPL. The pharmacists perceived patients as more active in the meeting when using the QPL. The list focused the conversation on medications, which the pharmacists appreciated from a professional point of view. They described the QPL as a useful tool that could easily be integrated into the dispensing process and required little training; however, challenges described were, for example, time constraints and stress. CONCLUSIONS: Pharmacists reported that using a QPL improved patient participation in the encounter. Encouraging counseling on medications was seen as beneficial from a professional point of view. In the early adoption phase, the QPL was easy to implement and did not increase the pharmacists' workload. A QPL appears to be a promising tool for pharmacists to improve the quality of the consultation experience.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 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".