Income after cancer across gender and age among Canadian adolescents and young adults
Bibliographic record
Abstract
BACKGROUND: Cancer in adolescents and young adults emerges during critical transitional phases, resulting in lasting effects on financial well-being. It remains uncertain whether cancer in adolescents and young adults exhibits differences in financial impact on income based on gender and diagnosis age over time. METHODS: We linked Canada's national cancer registry to personal tax records to identify adolescents and young adults (aged 15-39 years) diagnosed between 1994 and 2013. In the year before diagnosis, survivors were variable-ratio matched to 10 cancer-free individuals on several sociodemographic characteristics. Participants were followed longitudinally up to 10-years postdiagnosis or until 2015. Relative and absolute income changes were estimated using doubly robust difference-in-differences. We categorized age into 3 groups: adolescents (aged 15-17 years), emerging young adults (aged 18-29 years), and young adults (aged 30-39 years), reflecting the different adolescent and young adult life stages. Analyses were stratified by gender and diagnosis age. RESULTS: There were 60 240 women and 33 085 men survivors matched to 490 645 and 274 595 cancer-free participants, respectively. Overall, men and women had 6.9% (95% confidence interval [CI] = 5.1% to 8.6%) and 4.5% (95% CI = 3.1% to 5.8%) income reductions, respectively. Adolescent men had the largest reduction of 23.7% (95% CI = 1.9% to 40.6%), while a lack of statistical significance was observed in women of the same age. Income was reduced for varying magnitudes and durations across the different intersections of gender and diagnosis age, with men experiencing longer periods of income reductions. CONCLUSIONS: Cancer impacts income generation differently for adolescent and young adult men and women and at various diagnosis ages over time. Men, particularly younger men, are most vulnerable to income reductions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".