Shared decision-making with patients with complex care needs: a scoping review
Bibliographic record
Abstract
BACKGROUND: A number of patients have complex care needs that arise from interactions among multiple factors, such as multimorbidity, mental health issues, and social vulnerability. These factors influence decisions about healthcare and health services. Shared decision-making (SDM), a collaborative process between patients and professionals, is known to improve the quality of the decision-making process. However, follow-up challenges of patients with complex care needs (PCCNs) can lead to SDM specificities. OBJECTIVE: To identify specificities of SDM with PCCNs. METHODS: We conducted a scoping review using the Joanna Briggs Institute (JBI) methodology. We conducted a systematic search across MEDLINE, CINAHL, PsycINFO, and Academic Search Complete databases. Empirical studies about SDM with PCCNs published between 1997 and 2023 were eligible for inclusion. We conducted a mixed thematic analysis using deductive (Ottawa Decision Support Framework and Interprofessional Shared Decision-Making Model) and inductive approaches. Following Arksey & O'Malley's and Levac et al.'s methodological recommendations, we consulted experts (researchers, healthcare professionals, and patient partners) to enhance the findings. RESULTS: Twelve studies were included in the review. Overall, our results demonstrated the importance of recognizing some specificities of SDM with PCCNs, such as the simultaneous presence of multiple decisions and the multidisciplinary and intersectoral nature of the healthcare and health services they receive. CONCLUSION: This scoping review highlights some specificities that must be considered in SDM with PCCNs to maintain its already-known benefits and ensure positive health and decision-making outcomes.
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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 | 0.036 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".