Adaptation, Feasibility, and Acceptability of a Health Insurance Literacy Intervention for Caregivers of Pediatric Cancer Patients (CHAT-C)
Bibliographic record
Abstract
We adapted CHAT, a four-session virtual program to help individuals affected by cancer manage insurance and medical costs for caregivers of pediatric cancer patients (called CHAT-C); we then pilot-tested CHAT-C. Eligible caregivers were ages 18+ and the primary caregiver to a pediatric cancer patient (≤25 years old) diagnosed in the past five years and treated at Primary Children’s Hospital. We conducted engagement studios to adapt the program. Feedback was evaluated using a rapid qualitative analysis framework and included content preferences, navigator preferences, logistics/structure, timing of delivery, and feasibility/acceptability. A small pilot test of CHAT-C was conducted; feasibility, acceptability, and preliminary efficacy were evaluated based on enrollment rates, qualitative feedback, and baseline/follow-up surveys. Participants in the pilot (n = 14) were primarily white (93%), married (93%), female (86%), ages 40–49 (50%), and college-educated (57%). Most participants (64%) completed all four sessions of CHAT-C. Those who did not complete the sessions cited a lack of time, a child’s disease progression, and a perceived lack of benefit. Health insurance literacy (measured by nine items) improved by 10.8 points on average (SD = 6.0, range: 9–36) after CHAT-C. Caregivers of childhood cancer patients are willing to participate in a health insurance program, but some caregivers need less time-intensive options.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".