Abstract B096: Responding to stakeholder needs for cancer screening and prevention: Using formative evaluation to tailor outreach and navigation programming in Connecticut
Bibliographic record
Abstract
Abstract Background: Total cancer incidence rates in Connecticut are well above the national average, with disproportionate impact on our underserved and minority communities. As part of the Yale Cancer Center's (YCC) Community Outreach and Engagement activities, we translate population research into programmatic innovations designed to reach and address the needs of Connecticut's at-risk populations. Our recently initiated Yale Cancer Disparities Firewall (Firewall) project aims to strengthen population health through expanded outreach, navigation and health systems change. The project seeks to build a cancer disparities firewall around at-risk populations by addressing patient and system level factors across the cancer care continuum. The programmatic model consists of two primary intervention strategies—outreach and patient navigation—and local health infrastructure fortified by diverse partnerships and programmatic change. Prior to launching, a formative, baseline evaluation was conducted between January and May 2018. Findings from this evaluation are presented alongside proposed programmatic translations. Methodology: The baseline evaluation consisted of three components: 1) a review of existing programmatic data and data collection strategies, 2) analysis of current outreach and education activities and partnerships and 3) completion of seven stakeholder interviews. By triangulating findings across qualitative analyses, the evaluation, conducted by an external evaluator, identified potential facilitators for and barriers to project implementation and success. Findings were synthesized for translation into program activities as well as for pre-/post-implementation dissemination. Results and Translation: Baseline evaluation revealed four challenges to successful implementation: 1) limited input from residents “on the ground,” who might be the target of risk reduction and screening services, 2) presence of multiple levels of navigation and coordination for patients during and after cancer diagnoses, 3) few resources for helping would-be patients “navigate” risk reduction and screening services and 4) problematic race/ethnicity data. Despite these challenges, existing outreach and education activities consisted of 14 events, 1,909 contacts and 25 meetings with partner organizations. To incorporate the findings, our project has: 1) complemented existing community advisory board infrastructure with additional neighborhood-level input, 2) piloted community health navigation to draw new patients into cancer screening and risk reduction interventions and 3) initiated high-level meetings about whether and how collection and use of race/ethnicity data may be improved system-wide. Citation Format: Sakinah C. Suttiratana, Roy Herbst, Denise E. Stevens, Beth A. Jones. Responding to stakeholder needs for cancer screening and prevention: Using formative evaluation to tailor outreach and navigation programming in Connecticut [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr B096.
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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 | 0.185 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".