Abstract A059: The role of conversational chatbots in enhancing shared decision making for African American men with early stage prostate cancer
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».