Interventions addressing health-related social needs among patients with cancer
Bibliographic record
Abstract
Health-related social needs are prevalent among cancer patients; associated with substantial negative health consequences; and drive pervasive inequities in cancer incidence, severity, treatment choices and decisions, and outcomes. To address the lack of clinical trial evidence to guide health-related social needs interventions among cancer patients, the National Cancer Institute Cancer Care Delivery Research Steering Committee convened experts to participate in a clinical trials planning meeting with the goal of designing studies to screen for and address health-related social needs among cancer patients. In this commentary, we discuss the rationale for, and challenges of, designing and testing health-related social needs interventions in alignment with the National Academy of Sciences, Engineering, and Medicine 5As framework. Evidence for food, housing, utilities, interpersonal safety, and transportation health-related social needs interventions is analyzed. Evidence regarding health-related social needs and delivery of health-related social needs interventions differs in maturity and applicability to cancer context, with transportation problems having the most maturity and interpersonal safety the least. We offer practical recommendations for health-related social needs interventions among cancer patients and the caregivers, families, and friends who support their health-related social needs. Cross-cutting (ie, health-related social needs agnostic) recommendations include leveraging navigation (eg, people, technology) to identify, refer, and deliver health-related social needs interventions; addressing health-related social needs through multilevel interventions; and recognizing that health-related social needs are states, not traits, that fluctuate over time. Health-related social needs-specific interventions are recommended, and pros and cons of addressing more than one health-related social needs concurrently are characterized. Considerations for collaborating with community partners are highlighted. The need for careful planning, strong partners, and funding is stressed. Finally, we outline a future research agenda to address evidence gaps.
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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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".