Impact of health care costs on utilization of needed health care in the Childhood Cancer Survivor Study (CCSS).
Bibliographic record
Abstract
20 Background: Survivors of childhood cancer face increased risks for morbidity and premature mortality due to the sequelae of their primary disease and its treatment. Survivors may be socioeconomically vulnerable, which could negatively impact their healthcare utilization. We investigated sociodemographic factors associated with forgoing needed healthcare due to cost among long-term survivors within the CCSS. Methods: From a survey of insured and uninsured survivors conducted in 2011-12, forgoing needed healthcare due to cost was determined by the question “In the past year, was there a time when you did any of the following because you were worried about the cost? (Yes/No)” with 10 response categories (e.g., skipping a test/treatment, postponing medical care, taking a smaller cost of a prescription). ‘Yes’ responses were summarized using a categorical outcome variable of 0, 1-2, or ≥ 3 instances of forgoing healthcare. Ordinal logistic regression models assessed the association of sociodemographic factors with increased instances of forgoing healthcare due to cost, including survey weights to account for stratified sampling. Results: Of 1,110 mailed surveys, we received 698 (64%) responses. Mean age at time of survey was 39.6 (range 24-60) years and average time since diagnosis was 31.3 (SD = 4.6) years. 23% of survivors reported 1-2 instances of foregoing needed healthcare and 31% reported 3 or more. Odds of more forgone needed healthcare due to costs were increased for those who were uninsured, female, and had a chronic medical condition. [Table: see text]
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".