Unemployment following childhood cancer—a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Childhood cancer survivors are at risk of physical and mental long-term sequelae that may interfere with their employment situation in adulthood. We updated a systematic review from 2006 and assessed unemployment in adult childhood cancer survivors compared to the general population, and its predictors. METHODS: Systematic literature searches for articles published between February 2006 and August 2016 were performed in CINAHL, EMBASE, PubMed, PsycINFO, and SocINDEX. We extracted unemployment rates in studies with and without population controls (controlled /uncontrolled studies). Unemployment in controlled studies was evaluated using a meta-analytic approach. RESULTS: We included 56 studies, of which 27 were controlled studies. Approximately one in six survivors was unemployed. The overall meta-analysis of controlled studies showed that survivors were more likely to be unemployed than controls (Odds Ratio [OR] = 1.48, 95% confidence interval [CI]: [1.14; 1.93]). Elevated odds were found in survivors in the US and Canada (OR = 1.86, 95% CI: [1.26; 2.75]), as well as in Europe (OR = 1.39, 95% CI: [0.97; 1.97]). Survivors of brain tumors in particular were more likely to be unemployed (OR = 4.62, 95% CI: [2.56; 8.31]). Narrative synthesis across all included studies revealed younger age at study and diagnosis, female sex, radiotherapy, and physical late effects as further predictors of unemployment. CONCLUSION: Childhood cancer survivors are at considerable risk of unemployment in adulthood. They may benefit from psycho-social care services along the cancer trajectory to support labor market integration.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".