Financial Toxicity in Cancer Supportive Care: An International Survey
Bibliographic record
Abstract
PURPOSE: The study aims to explore unmet social needs and sources of financial toxicities in patients as noted by health care professionals and researchers in cancer supportive care, shedding light on potential health disparities. METHODS: In this cross-sectional survey, we anonymously surveyed active members of the Multinational Association of Supportive Care in Cancer (MASCC). The survey, structured in three sections, included questions regarding the routine assessment of social needs during patient consultations, sociodemographic aspects, factors influencing financial toxicity (FT), perceived support for managing FT, and available/desirable resources. RESULTS: A total of 218 MASCC members were included, predominantly from high-income countries (HIC, 73.4%), with many age 41-60 years (56.5%) and female (56.9%). Drug/treatment cost and insurance coverage were the main sources for FT among the HIC, whereas participants from low-middle-income countries (LMIC) considered transportation cost, loss of employment because of cancer diagnosis, and unavailability of return-to-work services as the top three sources of FT. Respondents from LMIC (adjusted odds ratio [aOR], 3.01 [95% CI, 1.15 to 7.93]) and physicians (aOR, 2.67 [95% CI, 1.15 to 6.21]) were more likely to routinely assess financial coverages. Socioeconomic status was consistently ranked as one of the top three sources of financial toxicities by participants from LMIC (34%), HIC excluding the United States (38%), those who do not self-identify as racial/ethnic minority (36%), and physicians (40%). CONCLUSION: This global survey of health care professionals and researchers in HIC and LMIC revealed varying approaches to assessing financial coverage and social needs. Socioeconomic status emerged as a consistent concern across countries, affecting financial toxicities. The study highlights the need for tailored approaches and improved resource visibility while emphasizing clinicians' pivotal role in addressing financial aspects of cancer care.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".