Impact of insurance type on survivor‐focused and general preventive health care utilization in adult survivors of childhood cancer
Bibliographic record
Abstract
BACKGROUND: Lack of health insurance is a key barrier to accessing care for chronic conditions and cancer screening. The influence of insurance type (private, public, none) on survivor-focused and general preventive health care in adult survivors of childhood cancer was examined. METHODS: The Childhood Cancer Survivor Study is a retrospective cohort study of childhood cancer survivors diagnosed between 1970 and 1986. Among 8425 adult survivors, the relative risk (RR) and 95% confidence interval (CI) of receiving survivor-focused and general preventive health care were estimated for uninsured (n = 1390) and publicly insured (n = 640), compared with for the privately insured (n = 6395) RESULTS: Uninsured survivors were less likely than those privately insured to report a cancer-related visit (adjusted RR, 0.83; 95% CI, 0.75-0.91) or a cancer center visit (adjusted RR, 0.83; 95% CI, 0.71-0.98). Uninsured survivors had lower levels of utilization in all measures of care in comparison with privately insured. In contrast, publicly insured survivors were more likely to report a cancer-related visit (adjusted RR, 1.22; 95% CI, 1.11-1.35) or a cancer center visit (adjusted RR, 1.41; 95% CI, 1.18-1.70) than were privately insured survivors. Although publicly insured survivors had similar utilization of general health examinations, they were less likely to report a Papanicolaou test or a dental examinations CONCLUSIONS: Among this large, socioeconomically diverse cohort, publicly insured survivors utilize survivor-focused health care at rates at least as high as survivors with private insurance. Uninsured survivors have lower utilization of both survivor-focused and general preventive health care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".