Abstract A38: Participatory cancer education through illustrated story maps to address cancer health disparities
Bibliographic record
Abstract
Abstract Background: Many racial and ethnic minorities consider cancer to be a death sentence, will not speak of it, and shun those who have it. Community use of focused, illustrated “story maps,” i.e. visual storytelling using imagery, to share experiences with cancer and start conversations about cancer may help to overcome barriers to cancer prevention and control, enhanced survivorship, and quality of life, such as alleviating fear and helplessness. The purpose was to develop and integrate story maps into an existing cancer health disparities educational program in churches. Methods: Building on a community-based participatory research approach and using formative research methods, a story map was produced depicting origins of cancer, cancer prevention and control, treatment, and survivorship emphasizing quality of life throughout. Focus groups (n=11) and feedback sessions with individuals and groups of stakeholders were conducted over an 18-month period to iteratively develop the maps with a graphic artist. Notes were maintained and used to guide development and revisions. An overall story map was developed with three smaller maps depicting sections describing cancer prevention, screening, and outcomes/treatment/survivorship. Maps are available in banner, large, and placemat size and on electronic media for projection. Church program facilitators were trained on how to use the story maps as part of the cancer educational program. A brief video was produced to provide additional training and technical assistance. Results: Facilitators are currently using the maps to address cancer-related health disparities in the African-American community. Sessions using the maps are designed to last one hour but often go longer due to participant dialogue. Facilitators guide the sessions focusing participants on the graphics and caricatures on the maps. Discussion begins by asking participants what they see on the maps. Responses typically lead to personal discussions and stories about cancer. Questions about how individuals, and families, stay on “the long-life highway” (shown on the map) and out of “the stream of cancer” (also shown on the map) are posed and stories shared. Pre- and post-test evaluation of the integration of the maps into the program, effect of the maps on intentions to prevent and control cancer as well as attitudes, beliefs, and fatalism, and efforts to address cancer disparities is being conducted. Conclusion: The process of developing the story maps was participatory and iterative, thus allowing the process to incorporate community beliefs into the maps to prompt discussion. From our previous use of story maps and the excitement shown by the facilitators, we anticipate positive evaluation results on the use of the maps. We expect to show significant improvement in self-efficacy, lower cancer fatalism, and equal or greater indications of intention to act on cancer information. Local, participatory communication appears to be more effective and credible in promoting healthful change to address cancer-related health disparities. Citation Format: John R. Ureda, Kimberly C. Rawlinson, Heather M. Brandt, Wanda Green, Deloris G. Williams, Andrea S. Gibson. Participatory cancer education through illustrated story maps to address cancer health disparities. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr A38.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".