Design and Validation of a Chatbot-Based Cervical Cancer Screening Decision Aid for Women Experiencing Socioeconomic Disadvantage: User-Centered Approach Study
Notice bibliographique
Résumé
BACKGROUND: Cervical cancer (CC) screening participation remains suboptimal among vulnerable populations in France. This study aimed to develop and evaluate AppDate-You, a chatbot-based decision aid, to support women from socioeconomically disadvantaged areas in the French Occitanie region to make informed decisions about CC screening, particularly human papillomavirus self-sampling (HPVss). OBJECTIVE: This study aimed to explore the needs, preferences, and barriers related to CC screening and to design and validate a user-centered, empathetic, and effective chatbot-based decision aid to empower women experiencing socioeconomic challenges in France to make informed choices about HPVss. METHODS: The chatbot was developed following a validated framework for developing decision aids. The process included qualitative research involving online and in-person interviews and focus groups with women and health care professionals, followed by alpha testing with both groups and beta testing with women only. Participants included women (both French and non-French speaking) aged between 30 and 65 years from socioeconomically disadvantaged areas of the Occitanie region and health care professionals (general practitioners, gynecologists, and midwives) working with these populations. AppDate-You was made accessible through WhatsApp and Facebook Messenger, offering text-based and voice-based interactions and multimedia content. RESULTS: The exploratory phase identified key barriers to screening and digital tool preferences. Prototype testing revealed great satisfaction with the chatbot's performance, educational value, and content quality. Contrary to the expectations of health care professionals, women from diverse backgrounds, including women who were older and socioeconomically disadvantaged, were willing and able to use the tool. Users-even those with limited digital literacy-found AppDate-You innovative, user-friendly, and informative. In the beta testing phase, 80% (12/15) of the participants expressed interest in HPVss. Some limitations were identified, such as the chatbot's occasional repetitive responses and the need for clearer medical terminology. CONCLUSIONS: This study demonstrates the potential for artificial intelligence chatbots to improve access to health education and increase cervical screening intention among underserved populations. The user-centered approach resulted in a tool that effectively meets the needs of the target population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/39288.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».