Shared Decision Making Among Racially and/or Ethnically Diverse Populations in Primary Care: A Scoping Review of Barriers and Facilitators
Bibliographic record
Abstract
PURPOSE: Disparities in the use of shared decision making (SDM) affect minoritized patients. We sought to identify the barriers and facilitators to SDM among diverse patients. METHODS: We conducted a scoping review in adherence to the Joanna Briggs Institute's methodologic framework. Our search of 4 databases-PubMed, Scopus, CINAHL Plus with Full Text, and PsycINFO-used controlled vocabulary and key word terms related to SDM in the care of racially and/or ethnically diverse adults in the primary care setting. We included peer-reviewed studies conducted in the United States or Canada that evaluated the process of decision making and that had populations in which more than 50% of patients were from racial and/or ethnic minorities. Unique records were uploaded to a screening platform for independent review by 2 team members. We used grounded theory to guide our inductive approach and completed a thematic analysis. RESULTS: A total of 39 studies met all inclusion criteria. We identified 5 overarching themes: (1) factors regarding the decision-making process during the clinical encounter, (2) clinician practice characteristics, (3) trust in the clinician/health care system, (4) cultural congruence between clinician and patient, and (5) extrinsic factors affecting the decision-making process. Barriers of SDM included cultural and language discordance; prejudice, bias, and stereotypes; mistrust; and clinician time constraints. Facilitators of SDM included cultural concordance between clinician and patient; clinician language competence; and clear, honest, and humanistic communication with patients having the ability to ask questions. CONCLUSIONS: We identified a set of potentially modifiable factors that facilitate or impede SDM. Our findings can help inform strategies and interventions to improve SDM among racially and/or ethnically diverse patient populations.
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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 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".