Exploring Posttraumatic Stress Symptoms and Posttraumatic Growth among Children Living beyond Cancer and Their Parents Using an Actor–Partner Interdependence Model
Bibliographic record
Abstract
There is a growing focus on describing both negative and positive outcomes in the wake of childhood cancer. The purpose of this study was to describe and explore the relationships between posttraumatic stress symptoms (PTSS) and posttraumatic growth (PTG) among children living beyond cancer and one of their parents. As part of a larger online survey, 113 children (Mage at time of study = 15.82 (SD = 4.81); Mage at diagnosis = 5.86 (SD = 4.66)) and one of their parents completed questionnaires assessing PTSS and PTG. Descriptive statistics were used to describe the sample and levels of PTSS and PTG. Data were z-transformed and analyzed using bivariate correlations and t-tests. An actor–partner interdependence model (APIM) was used to test whether children’s and their parents’ PTSS was associated with their own PTG (actor effect) and the others’ PTG (partner effect). PTSS was low and PTG was moderate in this sample relative to scale ranges. There were no significant differences between the children’s and their parents’ PTSS (p = 0.535) or PTG (p = 0.534). Results from the APIM showed no significant actor effects (p = 0.185). A significant overall partner effect (p = 0.020) emerged. Lower PTSS for children was associated with greater PTG for their parents (b = −0.29, p = 0.018), but parent’s PTSS was not associated with children’s PTG (p = 0.434). This sample reported similar levels of PTSS and PTG to that which has been reported in the literature. Children and their parents’ scores on PTSS and PTG measures were not significantly different from one another. Children’s PTSS was negatively associated with their parents PTG, illuminating the ways in which PTSS and PTG may be related in the context of childhood cancer. Exploring family-based strategies to reduce PTSS and enhance PTG may be warranted, though further studies are required.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".