shared decision-making with users with complex care needs: a scoping review
Bibliographic record
Abstract
Context 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, some challenges of patients with complex care needs (PCCN)s can lead to SDM specificities. Objective Identify specificities of SDM process with PCCNs. Study Design and Analysis Scoping review according to the Joanna Briggs Institute methodology with an expert’s consultation as proposed by Arksey et O’Malley. Data have been summarized based on Ottawa Decision Support Framework (ODSF) and Interprofessional SDM Model (IP-SDM) mixed thematic analysis (deductive and inductive). The results have been integrated during the experts’ consultation to collect data on professional practice and the integration of SDM for PCCNs. Setting Any context across PCCNs healthcare services utilization. Population Studied PCCNs are defined as patients presenting a combination of two or more elements of vulnerability (multiple concurrent chronic conditions, functional and cognitive impairments, mental health challenges and social vulnerability, individual characteristics, or major change in his life or care trajectory) Intervention/Instrument An extraction guide has been developed based on the domains of the ODSF and the IP-SDM, two validated SDM models. Outcome Measures Interprofessional approach, decision-making key factors, decision support needs, decision support interventions and assessment of process quality/decision-making outcomes will be described. Results Twelve studies were included in the review. Overall, our results demonstrated the importance of recognizing some specificities of shared decision-making with people with complex care needs, such as the simultaneous presence of multiple decisions and the interdisciplinary and intersectoral nature of the health care and health services they receive. Conclusions This scoping review highlights some specificities that must be considered in shared decision-making with people with complex care needs.
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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 | 0.048 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".