Psychological distress in grandparents of grandchildren who survived childhood cancer − Results from the GROkids project
Bibliographic record
Abstract
Introduction Having a grandchild who survived childhood cancer might affect grandparents' mental health. We aimed to A) describe the psychological distress of grandparents of childhood cancer survivors (CCS) and compare their distress to the Swiss general population, and B) explore the associations between the psychological distress of grandparents with person-, child-, and cancer-related characteristics. Methods This is a cross-sectional study conducted in Switzerland. Grandparents were identified from families of eligible CCS (cancer diagnosis before 18 years old; 3-10 years after diagnosis). A subsample of a representative sample for the Swiss general population was used for comparison similar in age, gender and language region. The Brief Symptom Inventory-18 (BSI-18) was administered to assess psychological distress on three domains: somatization, depression, anxiety; and a Global Severity Index [GSI]. We run Chi-squared and t-tests to compare grandparents and comparisons, and univariable, multivariable and multilevel regressions to analyze associations. Results In total, 122 grandparents (60.7% female, mean age=72.8; SD=6.8) and 354 comparisons participated (55.4% female; mean age=65.7; SD=5.5). Grandparents reported average distress levels and their scores did not differ significantly from the comparison sample (all p>.05). Grandparents with worse health perception described more psychological distress (somatization: β=6.86, p<.001; depression: β=4.17 p<.001; anxiety: β=5.87, p<.001; GSI: β=6.30, p<.001), while single grandparents experienced more depression than those in a partnership (β=-6.21, p=.013). Discussion Our findings are encouraging, showing adequate psychological health among grandparents of CCS. However, grandparents who perceived their health as poorer encounter higher levels of distress and may benefit from access to support groups and tailored informational material.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".