Mitigating Implicit Bias in Patient-Clinician Communication in Clinical Encounters: Protocol from the COmmuNity-engaged SimULation Training (CONSULT) Trial
Bibliographic record
Abstract
Abstract Background : Healthcare professionals have negative implicit biases toward minority and poor patients. Few communication skills interventions target implicit bias as a factor contributing to disparities in health outcomes. We report the protocol from the COmmuNity-engaged SimULation Training for Blood Pressure Control (CONSULT-BP), a trial evaluating a novel intervention targeting graduate medical and nursing trainees designed to mitigate the effects of implicit bias in clinical encounters. The CONSULT-BP intervention combines knowledge acquisition, bias awareness, and practice of bias mitigating skills in simulation-based communication encounters with racially/ethnically diverse standardized patients. The trial evaluates the effect of this 3-part program on patient BP outcomes, self-reported patient medication adherence, patient-reported quality of provider communication, and trainee bias awareness.Methods : We are conducting a cluster randomized trial of the intervention among cohorts of internal medicine (IM), family medicine (FM), and doctorate nurse practitioner (DNP) trainees at a single academic medical center. We are enrolling entire specialty cohorts of IM, FM, and NP trainees over a 3-year period, with each academic year constituting an intervention cycle. There are 3 cycles of implementation corresponding to 3 sequential academic years. Within each academic year, we randomize training times to 1 of 5 start dates using a stepped wedge design. The stepped wedge design compares outcomes within training clusters before and after the intervention, as well as across exposed and unexposed clusters. Primary outcome of blood pressure control is measured at the patient-level for patients clustered within trainees. Eligible patients for outcomes analysis are: English-speaking; non-White racial/ethnic minority; Medicaid recipient (regardless of race/ethnicity); hypertension; not have pregnancy, dementia, schizophrenia, bipolar illness, or other serious comorbidities that would interfere with hypertension self-control; not enrolled in hospice. Secondary outcomes include trainee bias awareness. A unique feature of this trial is the engagement of academic and community stakeholders to design, pilot test and implement a training program addressing healthcare.Discussion: Equipping clinicians with skills to mitigate implicit bias in clinical encounters is crucial to addressing persistent disparities in healthcare outcomes. Our novel, integrated approach may improve patient outcomes.Trial registration : NCT03375918Protocol version : 2.0 (October 16, 2020)
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".