A graphical model to make explicit pharmacist clinical reasoning during medication review
Bibliographic record
Abstract
Pharmacists' roles have evolved substantially from traditional drug compounding and dispensing to encompass patient-centred clinical services. Pharmacist clinical reasoning, though fundamental to these new roles, generally remains implicit and understudied, particularly compared with that of other healthcare professionals, such as physicians. However, teaching and supervising the clinical services provided by pharmacists require a thorough understanding of the reasoning process involved. Several models describing pharmacist clinical reasoning have been developed, but they lack unified mapping. Here, we used an instrumental case study approach to develop a model of pharmacist clinical reasoning during medication review. Our model is adapted from a previously published modelling-using-typified-objects model of physician clinical reasoning in all its cognitive complexity. Our pharmacist model, validated after iterative development and expert consultation, aligns components of pharmacist clinical reasoning with those of physician clinical reasoning. The clinical case contains drug-related problems of variable clinical relevance, as well as numerous key elements (e.g., laboratory results, vital signs) necessary for conducting a medication review. The case serves both as the foundation for model development and as an illustrative step-by-step example within this article. Our model delineates the subprocesses of pharmacist clinical reasoning during medication review, offering a flexible, multipath structure that underscores the dynamic, nonlinear nature of the reasoning. The model might be able to clarify implicit cognitive processes, thus furthering the overarching objective of promoting reflective skill development among learners rather than relying solely on tacit knowledge gained through practice 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".