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Financial hardship in adult survivors of childhood cancer: A report from the Childhood Cancer Survivor Study (CCSS).

2021· article· en· W3171968864 on OpenAlexaff
Paul C. Nathan, I‐Chan Huang, Yan Chen, Tara O. Henderson, Elyse R. Park, Anne C. Kirchhoff, Leslie L. Robison, Kevin R. Krull, Gregory T. Armstrong, Wendy M. Leisenring, Rena M. Conti, Yutaka Yasui, K. Robin Yabroff

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of AlbertaHospital for Sick Children
FundersNational Institutes of Health
KeywordsMedicineWorryCancerFinanceDemographyGerontologyPsychiatryInternal medicineAnxiety

Abstract

fetched live from OpenAlex

10026 Background: The impact of childhood and adolescent cancer on the long-term financial outcomes of survivors is poorly understood. We compared financial hardship between survivors and siblings enrolled in the CCSS and identified survivors at elevated risk. Methods: Survivors treated for cancer at age < 21 years in 1970-1999 and siblings responded to a survey (23 binary-response questions) at age ≥26 years administered in 2017-2019. Principal component analysis with promax rotation extracted 3 factors with eigenvalues > 1 and KR-20 reliability coefficients > 0.7, retaining items with factor loadings > 0.4. These factors were behavioral hardship (8 items, e.g., forgone needed medical care), material hardship/financial sacrifices (8 items, e.g., problems paying medical bills) and psychological hardship (3 items, e.g., worry about having enough money to pay rent/mortgage). Factor scores were calculated by adding the item responses and dividing by their standard deviation. Multiple linear regression examined the association of sociodemographic and cancer treatment variables with factor scores. Results: Among 3349 survivors (49% male; median age [range] 40.2 [26.0-67.4] years) and 976 siblings (42% male, median age 46.5 [ 26.1-69.2] years), survivors were more likely to report being sent to debt collection (29.5 vs 21.4%), problems paying medical bills (20.0 vs 11.9%), foregoing needed medical care (13.3 vs 7.7%) and worry/stress about paying their mortgage (32.8 vs 23.2%) or having enough money to buy nutritious meals (25.0 vs 16.2%), all P < 0.001. Survivors reported greater hardship than siblings on all 3 factors: behavioral hardship (standardized mean score 0.51 vs 0.36), material hardship/financial sacrifices (0.63 vs 0.44), psychological hardship (0.69 vs 0.44), all P < 0.001. Behavioral hardship was increased by female gender (regression coefficient [ꞵ] 0.17, 95% CI 0.10-0.25), < high school (ꞵ 0.45, CI 0.12-0.79) or < college (ꞵ 0.18, CI 0.09-0.26) education, no (ꞵ 1.14, CI 0.93-1.35) or public (ꞵ 0.23, CI 0.10-0.35) health insurance, being divorced/separated (ꞵ 0.28, CI 0.10-0.46) and ≥250mg/m2 anthracycline chemotherapy (ꞵ 0.09, CI 0.00-0.19). The same variables were significantly associated with the other two hardship factors, but total body irradiation and cranial radiation also contributed to the risk of material hardship/financial sacrifices, and ≥8g/m2 cyclophosphamide equivalent dose and cranial radiation contributed to psychological hardship. Conclusions: Survivors of childhood and adolescent cancer are at elevated risk for financial hardship as compared to sibling controls. Those at highest risk can be defined using a combination of sociodemographic and treatment variables. This information can be used to inform targeted intervention strategies to reduce the risk of poor financial outcomes in this vulnerable population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.098
GPT teacher head0.461
Teacher spread0.363 · 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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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