Abstract B31: Creating a mobile device-based educational animation for African American women with hereditary breast cancer risk
Notice bibliographique
Résumé
Abstract Background: Women residing in predominantly African America (AA) communities on the south side of Chicago have a breast cancer (BC) mortality rate twice as high as women living in predominantly white communities on the north side of the city. The emerging precision health paradigm for BC control that bases screening and prevention on individual level of risk has the potential to narrow the mortality gap by providing effective enhanced screening and preventive measures to AA women at high risk. Implementing a precision medicine strategy will require cancer genetic risk assessment (CGRA) in the primary care setting and referral of women with familial BC risk for genetic counseling (GC). Our prior work with CGRA in primary care clinics in AA communities revealed that women with a family history of BC who meet criteria for genetic counseling are unlikely to attend a GC consultation even when they are referred by their primary care physician (PCP). We found a strong desire among AA women and their PCPs for culturally sensitive educational materials tailored to AA women at risk for hereditary BC to help them understand the purpose of genetic counseling. We are developing a scientific educational animation delivered on a mobile device platform that is designed to motivate AA women with familial BC risk to attend a genetic-counseling consultation. Methods: Scientific animations are an effective tool for educating individuals with low health literacy on the benefits of cancer screening. Scientific animations delivered on smart phones have been used successfully in low-resource countries to provide basic health information. The intervention will be a scientific animation that can be viewed on smart phones, which will be created through an iterative process and will incorporate key elements of culturally sensitive health behavior interventions. The initial step involves semistructured interviews to identify factors that motivated attendance or nonattendance at a GC consultation among AA women who meet national guidelines for genetic counseling based on family history of breast cancer and were referred for counseling by their PCP in an earlier study. The sample (n=20) includes both women who did and who did not attend a GC session. Themes identified in the qualitative interviews will be used to create the script for the animation. The script will be story-driven. We will conduct two “story circles” with a subgroup of women participating in the semistructured interviews. The story circle fosters a safe environment for learning across modes of intelligence, expertise, and praxis. Participants will be asked to relate their family's experience with breast cancer in a story format, and to describe how that story affected them. Findings from the story circles will augment themes identified in semistructured interviews to create a storyline, script, and artwork for the animation that is based on the participants' family experiences. We will then conduct focus groups with key stakeholders from local AA communities and AA women with family history of BC to elicit responses to the script, storyboards, and artwork, and revisions will be made as needed based on input from the focus groups. The animation is created in collaboration with the Scientific Animations without Borders, and we will test the final animation with the same participants who viewed the draft storyboards and artwork. Results: Semistructured interviews and the story circles will be completed by the end of July, 2017, and a draft of the script and initial artwork will be completed by September of 2017. Key themes and stories for the script and preliminary artwork for the animation will be presented. Conclusion: A technology-enabled, culturally sensitive scientific animation that motivates AA women with increased breast cancer risk to attend a genetic counseling consultation will facilitate implementation of a population-based, precision health approach to eliminating BC disparities. Citation Format: Zo Ramamonjiarivelo, DeLawnia Comer-Hagans, Ifeanyi Beverly Chukwudozie, Shirley Spencer, Vida Henderson, Barry Pittendrigh, Julia Bello-Bravo, Karriem S. Watson, Catherine Balthazar, Rupert Evans, Robert A. Winn, Angela Odoms-Young, Kent Hoskins. Creating a mobile device-based educational animation for African American women with hereditary breast cancer risk [abstract]. In: Proceedings of the Tenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2017 Sep 25-28; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2018;27(7 Suppl):Abstract nr B31.
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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 | 0,001 | 0,004 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,052 | 0,007 |
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 ».