Mental health patient‐reported outcomes among adolescents and young adult cancer survivors: A systematic review
Bibliographic record
Abstract
BACKGROUND: Adolescent and young adult (AYA) cancer patients and survivors face significant mental health challenges throughout their cancer journey that are different to those faced by children and older adults. Patient-reported outcome measures (PROMs) can be used to explore the experiences of AYAs, and to identify important issues and areas for potential improvement in quality of life. OBJECTIVE: We aimed to compare patient reported mental health outcomes between AYAs diagnosed with cancer and non-cancer controls. METHOD: We built on a larger systematic review of AYA cancer PROMs which searched PubMed, EMBASE, CINAHL and PsychINFO. This review identified 175 articles, which were filtered to those reporting on mental health and including a non-cancer control group. RESULTS: We identified 12 eligible studies. Seven studies (58%) found those diagnosed with cancer reported poorer mental health than the non-cancer controls. The remaining five (42%) studies found no significant difference in severity or prevalence of mental health between the AYA cancer cohort and the healthy control group. Most (83%) were cross-sectional studies, highlighting the need for further longitudinal assessment of this group throughout their journey. CONCLUSIONS: The mental health outcomes feature conflicting results and illustrate the need for larger studies to characterise discrepancies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| 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".