Mapping the Processes of Pharmacist Therapeutic Reasoning: A Scoping Review and Development of the Pharmacist Therapeutic Reasoning Model
Bibliographic record
Abstract
BACKGROUND: Pharmacists make complex therapeutic decisions. Yet the reasoning processes that result in these choices, therapeutic reasoning (TR), are poorly defined. Existing models of clinical reasoning often overlook how pharmacists weigh risks and benefits of treatment options. AIM: To develop a conceptual model that characterizes the processes, subprocesses, and cognitive strategies used during pharmacist TR based on current literature. METHODS: A scoping review was conducted in February 2024 to identify studies describing pharmacist or pharmacy student reasoning during therapeutic decision-making. Data were extracted by two researchers using a standardized form and inductively analyzed. Codes were thematically organized based on shared properties: discrete knowledge, reasoning connections, or modifying influences. Theory use was assessed using the Continuum of Theory Talk framework. RESULTS: Ten studies met inclusion criteria representing diverse contexts, scope, and reasoning stimuli. A total of 109 unique codes were identified and synthesized into a conceptual pharmacist therapeutic reasoning model (Pharm-TRv1). It consists of three knowledge domains (drug, disease, and patient information), three core reasoning processes connecting these domains (drug-patient, drug-disease, patient-disease), and three to four related subprocesses. The model includes five influencing factors: two external (decision context and entry and exit from reasoning) and three internal cognitive modifiers (metacognition, closing a knowledge gap, and reflection). CONCLUSION: Pharm-TRv1 provides a foundational model of pharmacist therapeutic reasoning grounded in current literature. It offers a structured way to describe, teach, and study how pharmacists evaluate treatment options. Future research should further explore specific processes and subprocesses, validate the model, and explore broader theoretical perspectives.
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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 | 0.076 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.053 | 0.041 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".