Factors Associated with Quality of Life (QoL) in Adolescents and Young Adults with Cancer
Bibliographic record
Abstract
Adolescents and young adults (AYAs) diagnosed with cancer between the ages of 15 and 39 years face unique challenges that impact their long-term quality of life (QoL). Physical functioning, relationship status, social support, anxiety, and depression have been associated with QoL in AYAs with cancer. This study explored factors associated with increased QoL in a sample of 392 AYAs with cancer in Canada who participated in the initial Young Adult Cancer Canada RECOVER survey. The EORTC QLQ-C30 was used to measure QoL. Adjusting for relevant demographic, cancer, and clinical variables, the only factors significantly associated with QoL in multivariable analysis were symptoms of depression [Mild (β = −0.420, p < 0.001); Moderate (β = −0.937, p < 0.001); Moderately Severe (β = −1.188, p < 0.001); Severe (β = −2.182, p < 0.001)] and generalized anxiety [Mild (β = −0.244, p = 0.012); Moderate (β = −0.420, p = 0.002); Moderately severe (β = −0.400, p = 0.012); Severe (β = −0.697, p = 0.010)], as well as having completed treatment (β = −0.347, p < 0.001). Age, gender, time since diagnosis, having children, education, income, fear of recurrence, and social support were not significantly associated with QoL. These results support the need for age-appropriate resources to help AYAs manage the long-term psychological impacts of cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".