A Phenomenological Approach to Financial Toxicity: The-Economic-Side Effect of Cancer
Bibliographic record
Abstract
The economic burden of chronic diseases such as cancer could negatively impact patients’ health and quality of life. The daily management of the disease results in economic needs that patients often face directly, which may lead to real toxicity, just defined as financial toxicity. This study aims to explore cancer patients’ experiences, emotions, opinions, and feelings related to the phenomenon of financial toxicity. A phenomenological qualitative descriptive study was conducted through face-to-face interviews with adult oncological patients. The sample (n = 20) was predominantly composed of females (with a meanly 58 years old) with breast cancer and in chemotherapy treatment. The most relevant topics that emerged from the patients’ experiences were the impact on work, the distance from the treatment centre, the economic efforts, the impact on the quality of life, and the healthcare workers’ support during the healthcare pathway. From the phenomenological analysis of the interviews, three main themes and seven related subthemes emerged. This study provided a phenomenological interpretation of financial toxicity in adult cancer patients and underlines that this issue involves families or caregivers, too. Financial problems appear relevant for those who experience cancer and should be included in a routine assessment by healthcare professionals.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".