Abstract B104: Identifying gaps in equitable cancer care: insights from US-based oncology professionals engaged in continuing medical education
Bibliographic record
Abstract
Abstract Introduction: A previous evaluation of a five-part accredited continuing medical education (CME) designed to inspire action for equitable cancer care amongst US-based multidisciplinary oncology care team members, showed significant improvements in relevant knowledge and confidence (pre to post activity). This study aimed to revisit the data collected in the scope of this project to identify remaining challenges in ensuring equitable cancer care is delivered to all patients, with underlying gaps in knowledge, confidence and attitude that may be addressed through future CME. Methods: A secondary analysis of data was performed on quantitative (case-based multiple-choice) responses collected from US-based HCPs who completed at least one of the five accredited CME activities that were part of the program titled “Addressing Racial Disparities in Cancer Care”. Questions assessed learners’ knowledge, confidence, and attitudes after exposure to the CME. Crosstabulations with chi-square statistical tests compared quantitative responses by sub-group. The analysis focused on post-CME data to identify remaining educational gaps, with the aim of informing the development of future CME programs. Results: The five-part CME education reached 1151 unique HCPs involved in cancer care, with activity participation varying between n=103 to n=433. The profession/specialty sub-group most represented in aggregated responses across the five-part CME were registered nurses specialized in hematology/oncology (n=575/1145, 50%). Across US regions, respondents were mostly located in South US (n=446/1150, 39%). A challenge in helping patients navigate socio- economic barriers to oncology care was found, with the following four related gaps affecting a portion of respondents post-CME exposure: 1) misconception that the most important determinant of treatment adherence is a patient’s perceived treatment efficacy rather than optimal patient-provider communication (n=57/400, 14%), 2) sub-optimal knowledge of the impact of a patient’s community and/or partner on influencing their treatment-seeking behavior (n=31/97, 32%), 3) sub-optimal knowledge of necessary steps to address the root cause of a patients’ missed appointments (n=36/227, 16%), 4) sub-optimal confidence in addressing a patients’ personal circumstances limiting their access to cancer-care (n=424/1151, 37%). Conclusions: This study identified remaining gaps in knowledge, confidence and attitude among US-based HCPs post-exposure to a CME activity aimed at promoting equitable cancer care. Considering these gaps were observed post-education, the actual proportion of US-based HCPs affected is likely underestimated. Future continuing learning initiatives aimed at addressing cancer care disparities in the US should consider targeting gaps identified in this study by challenging learners to re-consider their knowledge, beliefs and confidence in best approaches to navigating the socio-economic barriers hindering patients from seeking cancer care (e.g., via adaptive learning, peer-to-peer learning). Citation Format: Monica Augustyniak, Karen Eldridge, Jayne Gurtler, Benyam Muluneh, Amy DePue, Julia Rodriguez-O'Donnell, Stacy Atkinson, Sophie Péloquin, Ann Murphy, Patrice Lazure. Identifying gaps in equitable cancer care: insights from US-based oncology professionals engaged in continuing medical education [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B104.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".