Supporting Employment After Cancer: A Mixed-Methods Evaluation of a Vocational Integration Programme for Childhood, Adolescent, and Young Adult Cancer Survivors
Bibliographic record
Abstract
Childhood, adolescent, and young adult cancer (CAYAC) survivors often face challenges entering the workforce due to long-term physical, cognitive, and psychological late effects, defined as chronic health conditions resulting from cancer and its treatments. This study evaluated a vocational integration programme that addresses these barriers and promotes psychosocial well-being. The multidisciplinary intervention combined career guidance, soft-skills training, and a paid internship. Using a mixed-method design with questionnaires and semi-structured interviews, we assessed feasibility, satisfaction, and psychosocial outcomes. Thirteen participants (mean-age-at-diagnosis: 12.9 years, SD 5.2; mean-age-at-interview: 27.2 years, SD 5.3) reported over 40 late effects, mostly of moderate severity. Health-Related Quality of Life (HRQoL), measured by the SF-12, showed a Physical Component Score mean of 45.2 (SD 9.1) and a Mental Component Score mean of 43.5 (SD 11.2), indicating greater psychological impact. The programme received high satisfaction ratings (mean 8.3/10) and was described as motivating and valuable, enhancing self-confidence and career prospects. Social support emerged as a key facilitator, while participants noted the need for flexibility and individualised pacing. Despite a limited sample size and potential recruitment bias, this study provides preliminary insights into the feasibility and perceived value of tailored vocational programmes, emphasising the importance of adaptable, socially supportive interventions for CAYAC survivors.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".