Examining Potential Implicit Bias in Oncologist-Patient Communication (CONNECT): Protocol for an Observational 2-Site Study
Bibliographic record
Abstract
BACKGROUND: Compared with White patients, minoritized patients (Black and Hispanic patients) have a higher incidence of advanced solid cancers and have a higher mortality. These patients also report poor patient-centered communication and worse pain assessment and management. Although many factors contribute to these disparities, physician implicit bias may be a contributing factor. OBJECTIVE: The primary goal of this study is to evaluate the role of implicit bias among oncologists and examine the impact on racial or ethnic differences in objective assessments of communication with minority patients with advanced cancer. METHODS: To accomplish this goal, we plan to recruit 65 oncologists and 325 patients (5 patients per oncologist) with advanced solid cancer from ambulatory cancer clinics within the diverse settings of the Mount Sinai Health System in New York City and the Duke University Health System in Durham, NC. We audio record patient-oncologist encounters during a postimaging visit, with 3 encounters for each of the patients. We will analyze the recorded visits and compare the patient-centered communication content of these conversations. Immediately after the recorded visit (no more than 2 weeks later, in order to minimize recall bias), patients are required to complete a follow-up survey to evaluate patient-centered outcomes. A 3-month follow-up survey is used to assess pain levels and control, use of analgesics, and psychological distress. A 6-month follow-up survey is used to assess psychological distress. We administer the Implicit Association Test to oncologists to assess their level of implicit bias toward patients who identify as Black or Hispanic after we finish recording patient encounters. RESULTS: Funding from the National Cancer Institute was received in March 2021. Patient and oncologist recruitment began in March 2022. We have recruited all 65 oncologists in the study, and patient recruitment is ongoing. The study team plans to continue to enroll patients until March 2025. As of December 2024, we have enrolled 245 patients. We expect to publish the findings in October 2026. CONCLUSIONS: In this paper, we outline the study methods, describe the development of a codebook to assess pain conversations being used to evaluate primary and secondary outcomes, and discuss challenges and lessons learned throughout the study. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66086.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.060 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".