Quality of life among survivors of adolescent and young adult cancer in Canada: A Young Adults With Cancer in Their Prime (YACPRIME) study
Bibliographic record
Abstract
BACKGROUND: The quality of life (QoL) among survivors of adolescent and young adult (AYA) cancer may be compromised compared with that in the general population. In this study, the authors: 1) assessed for differences in QoL among a national study of AYA cancer survivors compared with the Canadian population and 2) explored the factors associated with poorer QoL in AYA cancer survivors. METHODS: For the current research, data from the Young Adults With Cancer in Their Prime study were used. QoL was measured using physical and mental component scores from a 12-item short-form health status measure. A comparison group was derived from the Canadian Community Health Survey. RESULTS: AYAs (n = 195; 17.8% male; mean ± SD: 35.62 ± 6.89 years on study, 6.48 ± 5.73 years from treatment) were compared with a comparison sample (n = 665; 21.2% male). Among survivors, 31.8% reported poor physical health, and 49.7% reported poor mental health. Compared with the general population, AYAs had significantly lower physical health (F[1,818] = 52.80; P = .00) and mental health (F[1,818] = 83.54; P = .00), controlling for sex and age. Multivariable logistic regression analyses revealed that an annual income level <$40,000 (adjusted odds ratio [AOR], 8.32; 95% CI, 2.85-24.30), poor sleep quality (AOR, 1.19; 95% CI, 1.06-1.33), worse body image (AOR, 1.08; 95% CI, 1.02-1.14), and higher social support (AOR, 1.02; 95% CI, 1.00-1.05) were significantly associated with poor physical health. Poor sleep quality (AOR, 1.22; 95% CI, 1.08-1.38), body image (AOR, 1.06; 95% CI, 1.01-1.12), fear of cancer recurrence (AOR, 1.13; 95% CI, 1.06-1.21) were associated with poor mental health. CONCLUSIONS: The QoL of AYAs requires urgent attention. Sleep, body image, and social support may be important modifiable targets for intervention to improve their QoL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".