Describing posttraumatic growth and exploring its correlates among survivors of adolescent and young adult cancer
Bibliographic record
Abstract
Background: Posttraumatic growth (PTG), characterized as positive psychological change in multiple domains occurring in the wake of significantly difficult or traumatic life events, has been documented among cancer survivors. Few researchers have explored PTG among survivors of adolescent and young adult cancer (AYAs). AYAs may differ in their experiences of PTG because of the different types of adversities (e.g., physical, mental, emotional, social, financial) they must deal with. We sought to describe PTG levels in a sample of AYAs, and explore the relationships between PTG and personal, medical, and behavioural factors. Methods: Eighty-eight AYAs (Mage=33+/-4.4 years; 75% female) who were on average 1.7 years (SD=1.4) post-treatment completed an online survey that included the Posttraumatic Growth Inventory (PTG-I), the Leisure Time Exercise Questionnaire, and a sociodemographic/medical questionnaire. Descriptive statistics and multiple regression models for total PTG and each of the five PTG-I subscales were conducted. Results: Total PTG levels were moderately high relative to the scale range (M=60.3, SD=14.7, scale range=0-105). Mean PTG-I subscale scores were moderate-to-high (M=2.1-3.3, SD=0.7-1.4, scale range=0-5), with participants reporting the highest growth in appreciation of life and lowest growth in spiritual change. Current age, sex, type of cancer, time since treatment, and physical activity (PA) participation did not account for a significant amount of variance in total PTG or PTG-I subscales (R2=.039-.114, ps>.05). Conclusions: Findings suggest AYAs experience PTG. Further exploration of this phenomena and its potential predicators, correlates, and determinants among AYAs drawing on theory is required as AYAs stand to benefit from experiencing PTG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 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".