‘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 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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".