Clinical Management of Financial Toxicity–Identifying Opportunities through Experiential Insights of Cancer Survivors, Caregivers, and Social Workers
Bibliographic record
Abstract
Perspectives of cancer survivors, caregivers, and social workers as key stakeholders on the clinical management of financial toxicity (FT) are critical to identify opportunities for better FT management. Semi-structured interviews (cancer survivors, caregivers) and a focus group (social workers) were undertaken using purposive sampling at a quaternary public hospital in Australia. People with any cancer diagnosis attending the hospital were eligible. Data were analysed using inductive-deductive content analysis techniques. Twenty-two stakeholders (n = 10 cancer survivors of mixed-cancer types, n = 5 caregivers, and n = 7 social workers) participated. Key findings included: (i) genuine concern for FT of cancer survivors and caregivers shown through practical support by health care and social workers; (ii) need for clarity of role and services; (iii) importance of timely information flow; and (iv) proactive navigation as a priority. While cancer survivors and caregivers received financial assistance and support from the hospital, the lack of synchronised, shared understanding of roles and services in relation to finance between cancer survivors, caregivers, and health professionals undermined the effectiveness and consistency of these services. A proactive approach to anticipate cancer survivors’ and caregivers’ needs is recommended. Future research may develop and evaluate initiatives to manage cancer survivors and families FT experiences and outcomes.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".