Perceived positive impact of cancer among long‐term survivors of childhood cancer: a report from the childhood cancer survivor study
Bibliographic record
Abstract
OBJECTIVE: Investigations examining psychosocial adjustment among childhood cancer survivors have focused primarily on negative effects and psychopathology. Emergent literature suggests the existence of positive impact or adjustment experienced after cancer, as well. The purpose of this study is to examine the distribution of Perceived Positive Impact (PPI) and its correlates in young adult survivors of childhood cancer. METHODS: 6425 survivors and 360 siblings completed a comprehensive health survey, inclusive of a modified version of the Post-traumatic Growth Inventory (PTGI) as a measure of PPI. Linear regression models were used to examine demographic, disease and treatment characteristics associated with PPI. RESULTS: Survivors were significantly more likely than siblings to report PPI. Endorsement of PPI was significantly greater among female and non-white survivors, and among survivors exposed to at least one intense therapy, a second malignancy or cancer recurrence. Survivors diagnosed at older ages and fewer years since diagnosis were more likely to report PPI. Income, education and marital/relationship status appeared to have varied relationships to PPI depending upon the subscale being evaluated. CONCLUSIONS: The existence and variability of PPI in survivors in this study suggest that individual characteristics, inclusive of race, gender, cancer type, intensity of treatment, age at diagnosis and time since diagnosis, have unique and specific associations with different aspects of perceived positive outcomes of childhood 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".