Shared decision-making practices and patient values in pharmacist outpatient care for rheumatic disease: A multiple correspondence analysis
Bibliographic record
Abstract
Purpose: To investigate the value-to-value relationships, relationship between values and patient background, continuation rate of treatment after shared decision-making (SDM), and disease status in order to clarify the values involved in drug therapy decisions for patients with rheumatic disease. Methods: We investigated patient values (efficacy of drug therapy [effectiveness], safety, economics, daily life, and other) and the continuance rate and disease status of treatment after 6 months in 94 patients with rheumatic disease aged ≥18 years who made decisions with pharmacists and physicians in the pharmacy outpatient clinic between September 2019 and April 2021. Multiple correspondence and K-means cluster analyses were performed to show the relationship between values and basic patient information. Results: Among the selected patients, 87% and 47% selected effectiveness for multiple selections and single selection, respectively. Effectiveness was at the center of the graph; three clusters containing other values were placed around it. History of allergy or side effects caused by biologics or Janus kinase inhibitors were in the safety cluster. The non-usage history of biologics or Janus kinase inhibitors was in the economic cluster. Conclusion: Effectiveness was the most important factor for patients with rheumatic disease; the values that patients consider important may shift from effectiveness to other values based on each patient’s subjective experience with the treatment and/or the stage of life in which they were treated. It is important to positively link patient values and information about the treatment plan in shared decision-making while establishing rapport with the patient.
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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 | 0.015 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".