‘I think this medicine actually killed my wife’: patient and family perspectives on shared decision-making to optimize medications and safety
Bibliographic record
Abstract
Background: This study explored the perspectives and experiences from patients and families around how patient/family preferences and priorities are considered in medication-related discussions and decisions within the healthcare system. Methods: We conducted a qualitative study using focus groups with residents of Southern Ontario and British Columbia ( N = 16). Three focus groups were conducted using a semi-structured focus group guide. The audiotaped focus group discussions were transcribed verbatim. A thematic analysis, using inductive coding, was completed. Results: A total of three main themes [and several sub-themes (and sub-sub-themes)] emerged from the data: patient and family expertise [ lived experience, information expert, and perceived expert roles (patient/family, healthcare provider)], perceived patient-centredness ( relationship qualities of healthcare provider and assumptions about patients), and system ( time, coordination and communication, and culture). Stories told by participants helped to clarify the relationships between the themes and sub-themes, leading to, what we understand as shared decision-making around medications and subsequent health outcomes. Conclusions: Our findings showed that shared decision-making resulted from both recognition and integration of the personal expertise of the patient and family in medications, and perceived patient-centredness. This is broadly consistent with the current conceptualization of evidence-based medicine. The stories told highlight the complex, dynamic, and nonlinear nature of shared decision-making for medications, and that patient priorities are not as integrated into shared decision-making about medications as we would hope. This suggests the need for developing a systematic process to elicit, record, and integrate patient preferences and priorities about medications to create space for a more patient-centred conversation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".