Social Determinants of Health in Metastatic Breast Cancer Care: A Podcast Exploring Challenges and Opportunities in the United States and Canada
Bibliographic record
Abstract
The influence of social determinants of health (SDOH) on clinical outcomes for patients with cancer has become increasingly clear in recent years. This podcast, featuring a breast surgeon from the USA and a breast medical oncologist from Canada, provides updated definitions of SDOH, discusses the importance of assessing for SDOH-related barriers to optimal, equitable care for patients with metastatic breast cancer (mBC), and offers practical solutions that individual practices can implement. While this discussion is focused on the USA and Canada, SDOH factors are salient for healthcare providers around the world, irrespective of the healthcare systems deployed in their individual countries. Key SDOH that can impact care or outcomes for patients living with mBC are discussed by the hosts, including patient socioeconomic status, transportation logistics, health literacy, and social support. The importance of establishing trust between care provider and patient is also examined, especially for racial and ethnic minorities who have earned concerns and mistrust of healthcare systems. To aid practices in addressing SDOH-related barriers, various resources are identified, including free screening tools and support offered by patient advocacy and nonprofit organizations. Despite the scope of SDOH-related challenges, practices can collaborate to achieve progress toward equitable care for patients with mBC. Podcast Video (MP4 122704 kb).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".