Unequal distribution of financial toxicity among people with cancer and its impact on access to care: a rapid review
Bibliographic record
Abstract
PURPOSE OF REVIEW: Research demonstrates that patients and their families often carry a good portion of the economic burden during and following cancer treatment, frequently resulting in implications for access to care. This rapid review summarizes how this knowledge has evolved in recent years. RECENT FINDINGS: The number of articles on patient financial burden is increasing, suggesting awareness about the growing impact of economic burden on patients. This is particularly evident when discussing out-of-pocket costs, and lost work for patients/caregivers. However, there is an increasing focus on 'foregone care' and 'financial distress'. Additionally, emerging literature is examining policies and approaches to screen and/or mitigate these patient financial risks, thereby improving access to care. There is also increasing focus on populations that shoulder a disproportionate financial burden, including ethnic minorities (blacks, Asians, Latinos) as well as those with lower socioeconomic status. Additionally, there is evidence that this burden also affects the middle class. SUMMARY: As healthcare budgets become stretched, especially during a pandemic, supportive programs benefiting the less fortunate often shrink, which impacts access to care. The emerging research on strategies with government or institutions to mitigate these burdens and access issues are both welcome and needed.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".