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Enregistrement W2913130829 · doi:10.1158/1538-7755.disp17-b31

Abstract B31: Creating a mobile device-based educational animation for African American women with hereditary breast cancer risk

2018· article· en· W2913130829 sur OpenAlexaff
Zo Ramamonjiarivelo, DeLawnia Comer-HaGans, Ifeanyi Beverly Chukwudozie, Shirley Spencer, Vida Henderson, Barry R. Pittendrigh, Julia Bello‐Bravo, Karriem S. Watson, Catherine Balthazar, Rupert Evans, Robert A. Winn, Angela Odoms‐Young, Kent Hoskins

Notice bibliographique

RevueCancer Epidemiology Biomarkers & Prevention · 2018
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueNutrition, Genetics, and Disease
Établissements canadiensEngineers Without Borders Canada
Organismes subventionnairesnon disponible
Mots-clésReferralMedicineGenetic counselingBreast cancerFamily medicineGenetic testingFamily historyHealth careNursingGerontologyCancerInternal medicine

Résumé

récupéré en direct d'OpenAlex

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.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,052
Score d'incertitude au seuil0,173

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0520,007

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,335
Écart entre enseignants0,318 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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