Abstract B31: Creating a mobile device-based educational animation for African American women with hereditary breast cancer risk
Bibliographic record
Abstract
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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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.007 |
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".