Evaluation of a Conference on Cancer-Related Financial and Legal Issues: A Potential Resource to Counter Financial Toxicity
Bibliographic record
Abstract
This study describes the conception, development, and growth of the Triage Cancer Conference hosted by Triage Cancer, a national nonprofit organization providing free legal and financial education to the cancer community. We conducted a retrospective analysis of post-conference participant surveys. Descriptive statistics were calculated for participant demographics, and acceptability, feasibility, and appropriateness were evaluated. From 2016-2021, 1239 participants attended the conference and completed post-conference surveys. Participants included social workers (33%), nurses (30%), and cancer patients/survivors (21%), with representation from over 48 states. Among those who reported race, 16% were Black, and 7% were Hispanic. For acceptability, more than 90% of participants felt that the conference content, instructors, and format were suitable and useful. For feasibility, more than 90% of participants felt that the material was useful, with 93-96% reporting that they were likely to share the information and 98% reporting that they would attend another triage cancer event. Appropriateness was also high, with >80-90% reporting that the sessions met the pre-defined objectives. Triage Cancer fills an important gap in mitigating financial toxicity, and formal evaluation of these programs allows us to build evidence of the role and impact of these existing resources. Future research should focus on adding validated patient-reported outcomes, longer-term follow-up, and ensuring inclusion and evaluation of outcome metrics among vulnerable populations.
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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 | 0.034 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".