Mitigating Implicit Bias in Patient-Clinician Communication in Clinical Encounters: Early Insights from the COmmuNity-engaged SimULation Training (CONSULT) Trial
Bibliographic record
Abstract
Abstract BackgroundTo address the gap in knowledge about how to design feasible and acceptable trainings for clinicians that aim to mitigate implicit bias in clinical encounters, we report the early insights from the COmmuNity-engaged SimULation Training for Blood Pressure Control (CONSULT) Trial.MethodsWe engaged academic and community stakeholders to design, pilot test and implement a training program addressing healthcare disparities knowledge, bias awareness, and communication skills focused on bias mitigation. A stepped wedge cluster randomized trial was developed to determine intervention dose effects. We assessed the CONSULT training program through structured feedback using online surveys, real-time comments, and individualized feedback from trainees, faculty and standardized patients.ResultsThe first training cohort completed the intervention (N = 64). Feedback prompted training program revisions as follows: reducing overall time burden and the number of implicit bias assessments; supplementing on-line learning with augmented in-person interactive sessions. Feedback also reinforced the critical importance of having highly skilled facilitators versed in implicit bias.ConclusionsIterative stakeholder engagement is essential for developing and revising educational interventions aimed at raising bias awareness and mitigating the effects of implicit bias.Trial RegistrationClinicalTrials.gov, NCT 03375918. Registered December 18, 2017, https://clinicaltrials.gov/ct2/show/NCT03375918?id=NCT+03375918&draw=2&rank=1
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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 | 0.053 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".