Breast Cancer-Related Financial Toxicity in Sri Lanka: Insights From a Lower Middle-Income Country With Free Universal Public Healthcare
Bibliographic record
Abstract
Financial toxicity (FT) describes either objective or perceived excess financial strain due to a cancer diagnosis on the well-being of patients, families, and society. The consequences of FT have been shown to span countries of varied economic tiers and diverse healthcare models. This study attempts to describe FT and its effects in a lower- to middle-income country delivering predominantly public nonfee-levying healthcare. This was a cross-sectional study involving 210 patients with breast cancer of any stage (I to IV), interviewed between 6 and 18 months from the date of diagnosis. Financial toxicity was highly prevalent with 81% reporting 3 or more on a scale of 1 to 5. Costs incurred for travelling (94%), out-of-hospital investigations (87%), and consultation fees outside the public system (81%) were the most common contributors to FT. Daily compromises for food and education were made by 30% and 20%, respectively, with loss of work seen in over one-third. Greater FT was seen with advanced cancer stage and increasing distance to the nearest radiotherapy unit (P = .008 and .01, respectively). Family and relatives were the most common form of financial support (77.6%). In conclusion, FT is substantial in our group, with many having to make daily compromises for basic needs. Many opt to visit the fee-levying private sector for at least some part of their care, despite the availability of an established public nonfee-levying healthcare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".