Navigating Life With High‐Grade Glioma: Experiences and Needs of Adolescents and Young Adults
Bibliographic record
Abstract
BACKGROUND: Adolescents and young adults (AYA, 18-39) with high-grade glioma (HGG) face unique challenges at a life stage focused on autonomy, careers, relationships, and family planning. AIM: This study explores their experiences to inform life-stage appropriate support and resources. METHODS: In this mixed-methods study, we surveyed AYA HGG patients at Princess Margaret Cancer Centre (PM) to assess symptom experiences and care satisfaction. Interviews further explored their illness experiences and needs. Descriptive statistics summarized survey data, and thematic analysis guided by Braun and Clarke's framework identified key interview themes. Triangulation compared survey and interview results for a comprehensive understanding. RESULTS: Seventeen participants (7 men, 10 women; mean age 30.57) completed surveys and interviews. Triangulation revealed typical AYA challenges, such as delays in education, careers, and relationships, along with HGG-specific issues. Three main themes emerged: (1) managing cognitive and treatment-related impacts on life goals, (2) addressing physical and cognitive impairments affecting relationships, and (3) navigating identity loss and independence due to neurological symptoms. CONCLUSIONS: These findings highlight the need for tailored interventions and educational support integrated into AYA HGG care pathways.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".