Education, Employment, and Financial Outcomes in Adolescent and Young Adult Cancer Survivors—A Systematic Review
Bibliographic record
Abstract
Adolescents and young adults (AYAs) with cancer face unique challenges. We aimed to describe (i) education, employment, and financial outcomes and (ii) determinants for adverse outcomes in AYA cancer survivors. We performed a systematic literature search. We included original research articles on AYA (15-39 years of age) cancer survivors (≥2 years after diagnosis) and our outcomes of interest. We narratively synthesized the results of the included articles. We included 35 articles (24 quantitative and 11 qualitative studies). Patients in education had to interrupt their education during cancer treatment, and re-entry after treatment was challenging. After treatment, most survivors were employed but started their employment at an older age than the general population. Overall, no disadvantages in income were found. Survivors reported more absent workdays than comparisons. We identified chemotherapy, radiotherapy, late effects or health problems, female sex, migration background, and lower education associated with adverse outcomes. Although most AYA cancer survivors were able to re-enter education and employment, they reported difficulties with re-entry and delays in their employment pathway. To facilitate successful re-entry, age-tailored support services should be developed and implemented.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".