Engaging Nurses in Effective Cost of Care Conversations to Address Cancer-Related Financial Toxicity: Results from an Exploratory Survey
Bibliographic record
Abstract
Few evidence-based trainings exist on how to equip healthcare providers, particularly nurses, with the skills to engage in cost of care conversations with patients/caregivers to mitigate the impact of cancer-related financial toxicity. This study evaluated a pilot training developed in collaboration with Triage Cancer® to prepare oncology nurses to identify and assist patients/caregivers facing financial and/or legal barriers to care. Ten pediatric oncology nurses completed the training and pre/post-surveys on behaviors related to financial and legal need screening, frequency and comfort level of answering questions, knowledge, and behavior changes, along with training evaluation questions. At baseline, six nurses reported never screening for financial needs and nine for legal needs. Following the training, seven nurses stated they were likely to screen for financial/legal needs. At six months post-training, nurses had referred 85 patients/caregivers to financial/legal navigation services. Comfort levels in answering financial/legal questions increased by 6.5 points and knowledge scores increased by 1.7 points post-training. Most nurses recommended this training to other healthcare providers who work with patients with cancer and their caregivers. This study highlights the importance of providing oncology nurses with resources to engage in cost of care conversations and oncology financial legal navigation programs to mitigate the impact of cancer-related financial toxicity.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".