Assessing the needs of those who serve the underserved: A national survey among cancer care clinicians.
Bibliographic record
Abstract
175 Background: In 2009, ASCO confirmed that addressing cancer care disparities is critical for the Society and committed to approaches to eliminate such disparities. Yet, gaps remain in identifying the best approaches to do so. It remains unknown which cancer care providers serve patients from “underserved populations'' (defined as individuals who have historically received inadequate health care and health care services), what unmet needs they experience in their cancer care delivery, and how best to engage and support these providers. The objectives of this study were to explore challenges faced by providers serving underserved patients to inform development of a broader online survey and identify solutions that ASCO can implement to better support these providers. Methods: A multi-phase mixed-methods approach was utilized. Phase 1 involved key informant semi-structured interviews with 12 oncology providers caring for adult patients in the US from April to May 2021. Phase 2 involved survey development based on themes identified in Phase 1. The survey assessed: provider needs; processes for eliciting, documenting, and addressing social and economic needs of patients; and how ASCO could best support these providers. Phase 3 involved email distribution of the online survey in May 2022 to 5800 individuals identified through ASCO’s customer database. Eligibility criteria included providing care for adults with cancer in the US and prior consent to receive ASCO survey communications. Results: Of 477 respondents, the majority were ASCO members (88%), in an academic practice (57%), medical oncologists (77%), non-Hispanic (89%) and/or Caucasian/White (67%) and had > 15 years’ clinical experience (57%). A majority (60%) provided ≥25% of their clinical time providing cancer care to underserved populations and routinely engaged with administration to secure resources (61%) and local community organizations to obtain services (42%) for patients. Most (43-77%) indicated that a social worker/case manager was primarily responsible for addressing patient social needs. The majority reported that identification and dissemination of best practices (55%) and development of a return-on-investment business model (60%) would best help address patient needs. Some respondents expressed a desire to collaborate with ASCO on policy reform (32%) and for ASCO to help build or strengthen partnerships with local initiatives (29%). Conclusions: This is the first US-based survey assessing barriers and solutions to delivering cancer care among underserved populations. The findings from this work provide insights about how ASCO can help equip practices to address the social needs of their patients. Further work will be conducted to develop and implement suggested solutions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".