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Financial hardship among siblings of long-term survivors of childhood cancer: A Childhood Cancer Survivor Study (CCSS) report.

2023· article· en· W4379283247 on OpenAlexaff
Timothy J. D. Ohlsen, Huiqi Wang, David Buchbinder, I‐Chan Huang, Arti D. Desai, Zhiyuan Zheng, Anne C. Kirchhoff, Elyse R. Park, Kevin R. Krull, Rena M. Conti, Yutaka Yasui, Wendy M. Leisenring, Gregory T. Armstrong, K. Robin Yabroff, Paul C. Nathan, Eric J. Chow

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick Children
FundersNational Institutes of Health
KeywordsMedicinePsychosocialSiblingDemographyLogistic regressionEthnic groupNational Health Interview SurveyMarital statusHealth and Retirement StudyGerontologyHousehold incomeFinancePsychologyPsychiatryEnvironmental healthPopulationDevelopmental psychology

Abstract

fetched live from OpenAlex

10048 Background: Siblings of children with cancer may experience adverse household economic and psychosocial impacts during and after treatment. While many long-term survivors of childhood cancer experience financial hardship, financial outcomes of siblings in later adulthood are unknown. Methods: We surveyed randomly selected nearest age siblings of survivors (aged 18-64y) enrolled in the CCSS to estimate the prevalence of financial hardship, using 20 items adapted from validated national surveys grouped into 3 domains (material, psychological, behavioral). We calculated the prevalence of reporting any hardship within each domain. Multivariable logistic regression estimated associations between sibling sociodemographic characteristics and each domain of hardship. For individual financial hardship items with a matching item in the contemporaneous National Health Interview Survey (n = 21,271) or Behavioral Risk Factor Surveillance System (n = 259,901), we compared siblings with national survey respondents aged 18-64, calculating adjusted prevalence ratios to sample-weighted responses, adjusted for sex, race, ethnicity, household income, education, and marital status. Results: 1,008 siblings participated (of 1,590 approached; 63%) with median age of 46y (IQR 39–53y). Siblings were 57% female, 89% non-Hispanic White, 69% college-educated, and 74% married. The prevalence of reporting any material, psychological, and behavioral hardship among siblings was 34%, 28%, and 23%, respectively. Sibling factors associated with reporting material financial hardship included: female sex (OR 1.7, 95% CI 1.2–2.4), age (30–39y vs 21–29y: OR 2.61, 95% CI 1.1–6.5), household income ($50,000–$74,999 vs ≥$75,000: OR 2.3, 95% CI 1.4–3.6), lack of health insurance (OR 2.4, 95% CI 1.1–5.1), presence of severe/disabling chronic medical conditions (OR 1.7, 95% CI 1.1–2.8), out-of-pocket medical expenses ≥10% of income (vs < 10%: OR 4.9, 95% CI 1.9–12.3), and increased non-medical/non-home debt (for example, $25,000–$49,999 vs none: OR 5.2, 95% CI 3.2–8.6). Associations with psychological and behavioral hardship were similar. Compared with national survey respondents, siblings were more likely to report financial hardship on several individual items: difficulty paying medical bills (PR 1.2, 95% CI 1.0–1.4), worries about bills (PR 1.1, 95% CI 1.1–1.2), foregoing medical care (PR 1.3, 95% CI 1.0–1.6) and dental care (PR 1.4, 95% CI 1.2–1.6) due to cost, and worries about affording nutritious foods (PR 1.7, 95% CI 1.5–2.0). Conclusions: Adult siblings of childhood cancer survivors are more likely to experience multiple aspects of financial hardship than US adults in the general population, suggesting that childhood cancer may have life-long impact on siblings. Increased support for families across the trajectory of cancer treatment may help prevent or reduce financial hardship.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.124
GPT teacher head0.481
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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