Adolescent and Young Adult Perspectives on Challenges and Improvements to Cancer Survivorship Care: How Are We Doing?
Bibliographic record
Abstract
Purpose: The purpose was to review main challenges experienced by adolescent and young adult (AYA) cancer survivors (18–34 years) during transition to survivorship and their suggestions regarding improvements needed in care. Methods: A national survey was conducted to identify experiences with follow-up care 1–3 years after cancer treatment. The survey included open-ended questions for respondents to add topics of importance and details for deeper insight. This study presents analysis of open-ended questions about main challenge faced by AYA respondents and their suggestions for improvements in care. Results: Of 575 AYA survey respondents, 497 (86.4%) commented regarding main challenges. Twenty-one indicated that they had no challenges. Of those reporting challenges, 209 (43.9%) named one challenge, 267 (56.1%) identified more than one. In total, 955 challenges were identified with the most frequently cited being physical ( n = 462, 48.4%) and psychological ( n = 234, 24.5%). A total of 391 survivors wrote 679 suggestions about improvements in care with the majority ( n = 248, 69.4%) offering more than one. The most frequently cited suggestions included information/communication ( n = 191, 29.8%), naming a range of topics for which information was desired, and access to post-treatment therapies/services ( n = 164, 25.5%) such as counseling, physiotherapy, and occupational therapy. The overarching theme was, “I need follow-up care that fits me.” Conclusions: AYA cancer survivors are diverse and face unique challenges following treatment, which can have life-long implications and impede their recovery. Personalized follow-up care is highly recommended by these survivors.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".