Long-term effects of cancer on earnings of childhood, adolescent and young adult cancer survivors – a population-based study from British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: The patterns and determinants of long-term income among young people surviving cancer, and differences compared to peers, have not yet been fully explored. The objectives of this paper are to describe long-term income among young survivors of cancer, the impact of socio-demographic, disease, and treatment factors on long-term income, and income relative to the general population. METHODS: Retrospective cohort study with comparison group from the general population, using linked population-based registries, clinical data, and tax-records. Multivariate random effects regression models were used to determine survivor income, compare long-term income between survivors and comparators, and assess income determinants. Subjects included all residents of British Columbia (BC), Canada, diagnosed with cancer before 25 years of age and surviving 5 years or more. Comparators were selected from the BC general population matched by gender and birth year. RESULTS: Young cancer survivors earned significantly less than the general population. In addition, survivors of central nervous system tumors have significantly lower incomes than lymphoma survivors. Survivors who received radiation therapy have significantly lower income. Results should be interpreted with caution as the comparator group was matched by gender and date of birth. CONCLUSIONS: Depending on original diagnosis, treatment, and other characteristics, survivors face significantly lower income than peers and may require supports to gain and retain paid employment. Lower income will affect their opportunity for independent living, and will reduce productivity in the labour force.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".