Abstract A059: The role of conversational chatbots in enhancing shared decision making for African American men with early stage prostate cancer
Bibliographic record
Abstract
Abstract Introduction : Enhancing shared decision making (SDM) for African American (AA) men with early stage prostate cancer using conversational chatbots (CCs) aims to improve communication, provide information, and empower them in their treatment decision making process. SDM is a collaborative approach that involves patients and healthcare providers working together based on medical evidence and patient preferences. However, AA men face significant disparities in prostate cancer (PCa) outcomes, including higher incidence and mortality rates compared to other racial/ethnic groups in the United States. Limited access to recommended PCa information and inadequate information during SDM conversations contribute to the challenges AA men face when making treatment decisions. The purpose of this study was to develop a conversational chatbot (CC) to provide PCa information in real time thus, enhancing SDM for AA men with PCa. Methods: The Ottawa Decision Support Framework and the International Patient Decision Aids Standards provided guidance and framework for the development of the CC. Our participatory research approach involved a diverse Community-Based Participatory Research Advisory Board (CBPRAB) consisting of twelve members. The CBPRAB included AA PCa survivors, AA men, caregivers, advocates, community leaders, and cancer care providers. Through training in research methods and focus group facilitation, CBPRAB members actively contributed their valuable insights, ensuring a collaborative and inclusive research process. Results: Five stakeholder focus groups (n = 44) comprising AA men, PCa survivors, spouses, healthcare professionals, and advocates provided valuable input for the development of the CC. In total, 819 questions were generated through interviews conducted during the focus groups. These questions were either inferred from the discussions or explicitly provided by the participants. The answers to these questions were developed from evidence based sources, such as the American Cancer Society and National Comprehensive Cancer Network Guidelines. An Android-based smartphone application that allows users to submit questions via speech was created. The application matches the user's question within the data set of 819 questions and provides answers in both voice and text formats. Conclusion : The Android-based smartphone application was alpha tested on four stakeholder focus groups (n=35). The CC developed in the smartphone application demonstrated the capacity to provide real time responses to user questions sourced from an 819-question database derived from our stakeholder focus groups. By offering evidence based information and directing users to additional resources if the question is not in the database, the CC can improve accessibility to medical information, thus enhancing the SDM process regarding early stage prostate cancer. Citation Format: Angela D. Adams. The role of conversational chatbots in enhancing shared decision making for African American men with early stage prostate cancer [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A059.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".