Community pharmacists’ practices and clinical reasoning towards hospital discharge prescription: a study using simulations and retrospective think-aloud methodology
Bibliographic record
Abstract
BACKGROUND: The roles of community pharmacists have evolved from dispensing medications to clinical decision makers. This shift requires a clearer understanding of pharmacists' clinical reasoning. Managing hospital discharge prescriptions requires analytical reasoning to ensure patient safety through medication reconciliation and patient education. AIM: This study assessed community pharmacists' practices and their clinical reasoning towards hospital discharge prescriptions. METHOD: This mixed-method study consisted of two phases. First, community pharmacists participated in a simulated encounter in their pharmacy, where a patient presented a discharge prescription. Their practices and the structure of the encounter were assessed using a structured checklist of practices adapted from the MEDICODE checklist. Following the simulation, participants verbalised their thought processes in a retrospective think-aloud session. These semi-structured interviews were transcribed and analysed using both inductive and deductive qualitative methods. Charlin et al.'s model was used to assess clinical reasoning, while the Calgary-Cambridge model evaluated communication structure. RESULTS: Among 14 participating pharmacists, 13 performed medication reconciliation, and 10 contacted the simulated prescriber to address discrepancies. While most provided adherence aids, only seven assessed non-adherence, and five actively collaborated with the patient. Pharmacists exhibited diverse interview structures, often revisiting previous discussion points. Clinical reasoning misconceptions, such as assumptions or premature closure, were observed at multiple stages of the clinical reasoning process. CONCLUSION: Community pharmacists demonstrate strong medication-related skills but face challenges in clinical reasoning for discharge prescriptions. Clinical reasoning training, semi-structured consultations, and greater patient engagement would help tailor and improve post-discharge care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.233 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".