Informed shared decision making: An exploratory study in pharmacy
Bibliographic record
Abstract
INTRODUCTION: A study was undertaken to examine the feasibility of using the physician-based Informed Shared Decision Making (ISDM) framework for teaching pharmacy students competencies to effectively develop therapeutic relationships with patients. OBJECTIVES: TO: (1) assess the relevance and importance of the physician-developed ISDM competencies for pharmacy practice, (2) determine which competencies would be easiest and hardest to practice, (3) identify barriers to implementing ISDM in pharmacy practice, and (4) identify typical situations in which ISDM is or could be practiced. METHODS: Twenty pharmacists representing 4 different practices were interviewed using a standardized interview protocol. RESULTS: Pharmacists acknowledged that majority of the physician-based competencies were relevant to pharmacy practice; although not all competencies were considered to be most important. Competency #1 (Develop a partnership with the patient) was found to be the most relevant, the most important and the easiest to practice of all the competencies. While no one competency was identified as being hard to practice, there were several barriers identified to practicing ISDM. Finally, pharmacists expressed that patients with chronic conditions would be the most ideal for engaging in ISDM. CONCLUSION: While pharmacists believed that the ISDM model could provide a framework for pharmacists to develop therapeutic relationships with their patients, the group also identified obstacles to engaging successfully in this relationship.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".