Qualitative Examination of Shared Decision-Making in Canada’s Largest Health System: More Work to be Done : Shared Decision-Making—More Work to be Done
Bibliographic record
Abstract
Background: Shared Decision-Making (SDM) is an inclusive approach where patients and providers work in partnership to make health care decisions that are grounded in clinical best practice and align with patient preferences and values. Despite a growing recognition that SDM can lead to improved outcomes and reductions in unnecessary health investigations, tensions exist between patient agency and a historically paternalistic model of health care. As an evolving ideology, the Research Team sought to better understand the current state, challenges, and implementation opportunities of SDM practices across the health system. Methods: This study used a cross-sectional quality improvement design utilizing semistructured interviews to gather information from focus group participants. Five open-ended, qualitative questions were used to generate discussion on the perceptions of SDM and its role in clinical appropriateness in a variety of clinical contexts in our health system. A total of 12 focus groups (n = 95 participants) representative of patients and families, leaders, physicians, and frontline clinicians were engaged in the study. Results: Through a consensus-based approach, study results identified 4 recommendations based on 4 themes: Time, Communication, System Design, and Clinical Appropriateness. Conclusion: There are no easy solutions to the challenges of enabling SDM; however, success will be dependent upon recognizing the importance of patient agency, while maintaining an inclusive and continuous stakeholder engagement with both patients and providers. Implementation of the 4 recommendations at the organizational level highlighted in this study can serve as a road map for other health care institutions and will require a gradual approach to transform the general principles of SDM into tangible solutions to meet the emerging needs at both the local and system level.
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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 | 0.033 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.008 |
| 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".