Trajectories of Posttraumatic Growth Among Latvian Parents of Children with Cancer: A Mixed Methods Approach
Bibliographic record
Abstract
BACKGROUND: This study explores post-traumatic growth (PTG) among parents of childhood cancer survivors (CCSs), a group often underrepresented in research. METHOD: A convergent parallel mixed-methods design integrating Bayesian Multilevel Latent Class Analysis and Thematic Analysis was utilized in a longitudinal study involving 58 caregivers (50 mothers, 8 fathers) from the Children's Clinical University Hospital in Riga. Quantitative data were collected at diagnosis using the Psychosocial Assessment Tool (PAT) and Big Five Inventory-10 (BFI-10). Follow-up assessments post-treatment included the Responses to Stress Questionnaire (RSQ), Impact of Event Scale-Revised (IES-R), and the Post-traumatic Growth Inventory (PTGI). Qualitative data were collected through structured interviews. RESULTS: A 2-class model distinguished parents with low PTG from those with moderate to high PTG. Change in values, detachment from trivial stressors, and acceptance of life emerged as key indicators of growth. PTG was not significantly correlated with overall post-traumatic stress symptoms, but engagement coping strategies showed a positive association with PTG and personality traits like extraversion and openness. CONCLUSIONS: The mixed methods approach revealed sample-specific PTG elements not reflected in standardized tools. Initial perceptions of the cancer diagnosis shaped psychological outcomes, with PTG facilitated by adaptive coping, self-reflection, support, emotional disclosure, and psychological struggle. This study offers the first insights into PTG among Latvian parents of CCSs, a previously unexplored area.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".