Associations Among Health Behaviors and Psychosocial Outcomes in Adolescent and Young Adult Cancer Survivors
Bibliographic record
Abstract
Purpose: Adolescents and young adults (AYA) experience challenges both during and after their cancer treatment. Health behaviors are important contributors to health, yet little research examines health behaviors in AYA cancer survivors. We examined frequencies of health behaviors and associations between health behaviors, psychosocial, and clinical factors in AYA cancer survivors. Methods: Participants were survivors of AYA cancer (n = 60; 38.3% male; mean age = 25.3 years [standard deviation, SD = 4.6]; mean years since therapy completion = 9.0 [SD = 4.2]) from the Alberta Children's Hospital (ACH). Survivors were 13–21 years old at the time of diagnosis. Measures included demographic and clinical data, and the ACH Long-Term Survivor's Questionnaire. Health behaviors were compared with a control group (n = 600) using data from the 2017 Canadian Community Health Survey. Frequencies, conditional logistic regression, and logistic regression analyses were conducted. Results: Compared with controls, survivors reported engaging in physical activity (91.5% vs. 87.5%; odds ratio [OR] = 0.87, 95% confidence interval [CI] = 0.34–2.24; p = 0.77), smoking tobacco (15.3% vs. 19.7%; OR = 1.85, 95% CI = 0.89–3.85; p = 0.10), and street drug use (27.6% vs. 36.5%; OR = 1.60, 95% CI = 0.88–2.91; p = 0.12) at the same rate. Survivors reported binge drinking significantly less (61.0% vs. 76.6%; OR = 0.53, 95% CI = 0.30–0.92; p = 0.024) than controls. Logistic regression analyses revealed a significant model predicting binge drinking [χ2(5, 58) = 23.17, p < 0.001] with greater time off treatment, fear of another health condition, and higher mean body mass index emerging as significant predictors. Conclusion: AYA cancer survivors engage in risky health behaviors at rates similar to their peers. Further research is needed to understand factors mediating survivors' decision to participate in risky health behaviors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".