SUPPORT MY WAY: Supporting Young People After Treatment for Cancer: What Is Needed, When This Is Needed and How This Can Be Best Delivered
Bibliographic record
Abstract
As survival rates for teenagers and young adults (TYAs) with cancer exceed 80%, they are living longer post treatment, yet often experience prolonged health and quality of life concerns. Many TYAs also experience unmet support needs. This study aimed to identify TYAs support needs following treatment at a UK hospital and explore how and when TYAs prefer to receive support. This study involved two phases: Phase 1 involved semi-structured interviews with 16 TYAs, 1-6 years post-treatment, aged 16-25 years at time of treatment completion and examined their experiences of support services, and preferences for future care. Phase 2 consisted of co-design workshops with eight TYAs and feedback from five healthcare/allied professionals (HCAPs) to refine and develop recommendations. Phase 1 findings revealed six key themes: (1) survivorship as disrupted continuity; (2) negotiating legitimacy and relational safety in help seeking; (3) support offered vs. support sought: pathways of referral and self-initiation; (4) emotional readiness as context dependent and non-linear; (5) support as an ecosystem, not a moment; and (6) personalised autonomy in support engagement. Phase 2 findings informed recommendations that emphasise the importance of flexible, personalised, and accessible post-treatment support, with pathways of care/support that can adapt to TYAs changing needs and preferences over time.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".