Shared decision-making among diverse populations in primary care: a scoping review of facilitators and barriers
Bibliographic record
Abstract
Purpose: Disparities in the use of shared decision-making (SDM) affect minoritized patients. This scoping review seeks to identify the barriers and facilitators to SDM among diverse patients to help inform clinicians and improve SDM in primary care. Methods: We searched 4 databases: Scopus (Elsevier), CINAHL Plus with Full Text (EBSCO), and PsycINFO (EBSCO). This search combined controlled vocabulary and keyword terms related to SDM in the care of racially, ethnically, and culturally diverse patients in the primary care setting. We included peer-reviewed studies with original data based in the US and Canada on SDM in adults in primary care outpatient settings. We included papers about the process of decision making that included more than 50 % of their subjects as racial or ethnically diverse. Unique records were uploaded to a screening platform (Covidence) for independent review by 2 team members using these criteria. Grounded theory was used as an inductive approach to analyze themes. Results: 39 papers met all the inclusion criteria. We identified 5 overarching themes of SDM in diverse populations: Factors regarding the decision-making process during the clinical encounter, clinician practice characteristics, trust in the clinician/healthcare system, cultural congruence, and extrinsic factors affecting the decision-making process. Facilitators to SDM included cultural concordance, language competence, clear, honest, and humanistic communication, and having the ability to ask questions. Barriers included cultural discordance, language barriers, prejudice/bias/stereotypes, mistrust, and provider time constraints. Conclusions: Our findings highlight how health care providers and systems can support SDM in diverse populations within primary care.
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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 | 0.079 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 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".