Perceived Health among Adolescent and Young Adult Survivors of Childhood Cancer
Bibliographic record
Abstract
Survivors of childhood cancer (SCCs) are at increased risk of late effects, which are cancer- and treatment-related side-effects that are experienced months to years post-treatment and encapsulate a range of physical, cognitive and emotional problems including secondary malignancies. Perceived health can serve as an indicator of overall health. This study aims to (1) understand how a patient reported outcome (PRO) of perceived health of SCCs compares to controls who have not had a cancer diagnosis and (2) examine the relationships between perceived health and demographic and clinical variables, and health behavior. A total of 209 SCCs (n = 113 (54.10%) males; median age at diagnosis = 6.50 years; median time off treatment = 11.10 years; mean age at study = 19.00 years) were included. SCCs completed annual assessments as part of Long-Term Survivor Clinic appointments, including a question on perceived health answered on a five-point Likert scale. Data were collected retrospectively from medical charts. Perceived health of SCCs was compared to a control group (n = 836) using data from the 2014 Canadian Community Health Survey. Most SCCs (67%) reported excellent or very good health. The mean perceived health of SCCs (2.15 ± 0.91) was not statistically different from population controls (2.10 ± 0.87). Pain (B = 0.35; p < 0.001), physical activity (B = −0.39; p = 0.013) and concerns related to health resources (B = 0.59; p = 0.002) were significant predictors of perceived health. Factors shown to influence SCCs’ perceived health may inform interventions. Exploration into how SCCs develop their conception of health may be warranted.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".