The relationship between chronic health conditions and employment transitions among survivors of childhood cancer: A report from the Childhood Cancer Survivor Study (CCSS).
Bibliographic record
Abstract
10051 Background: Chronic health conditions are prevalent among adult survivors of childhood cancer. The impact of health on maintaining full-time (FT) employment, a common indicator of socioeconomic independence, has not been studied in this population. Methods: Self-reported employment status (FT, part-time [PT], unemployed [any reason], not in labor force) was assessed at two timepoints (2002-04 [T1] and 2015-16 [T2]) in adult (≥25y old) survivors of childhood cancer diagnosed between 1970-86. Sex-stratified Poisson regression, adjusted for race and ages at diagnosis and T2, was used to study associations between timing and severity of chronic health conditions (graded per the CTCAE v4.03) and transitions from FT to PT or unemployed. Results: Survivors employed FT at T1 (males=1712, median age [min-max]: 34y [25-53]; females=1337, 33y [25-53]) who reported employment status at T2 were included. At T2 (median time from T1 11.5y [9.4-13.8]), 83% males and 70% females remained employed FT, but 4% and 10% transitioned to PT, and 11% and 12% to unemployed (additional 2% and 8% left the labor force), respectively. Male and female survivors with grade 2 or 3-4 neurologic conditions acquired before T1 or between T1-T2 were at a higher risk of moving from FT to PT or unemployed compared to those with grade 0-1 conditions. Males and females with grade 3-4 respiratory conditions prior to T1 and cardiac and musculoskeletal conditions acquired between T1-T2 were also at higher risk for moving to PT or unemployed (Table). Additional predictors for males included grade 2 vision (before T1 RR 2.3, 95% CI 1.5-3.3; between T1-T2 RR 1.7, 95% CI 1.1-2.7) and endocrine (before T1 RR 1.4, 95% CI 1.1-1.9; between T1-T2 RR 1.7, 95% CI 1.3-2.3) conditions. Conclusions: A substantial portion of adult survivors of childhood cancer with health conditions of varying severity leave FT employment. Increased awareness of all stakeholders may facilitate access to clinical counseling and occupational provisions for flexible and supportive work accommodations to reduce work-related barriers for childhood cancer survivors. [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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".