Contributing factors to well-being in a sample of long-term survivors of childhood acute lymphoblastic leukemia: the role of social support in emotional regulation
Bibliographic record
Abstract
Objectives: To understand why some long-term childhood cancer survivors experience positive adjustment in the long run,[Q1] this study aimed to (1) explore associations between well-being, health status, social support, and emotion regulation (ER) strategies in a cohort of long-term childhood lymphoblastic leukemia (cALL) survivors, (2) identify the individual contribution of each ER strategy to well-being (3) and their interaction with social support.Methods: We used data from 92 participants from the PETALE cohort (51% female, aged 24 ± 7 years). Measures included well-being (WHO-5), health status (15D), social support (SSQ-6), cognitive reappraisal and expressive suppression (ERQ), and emotional processing and expression (EAC). We modeled the odds of high well-being adjusting for health status in logistic regressions and explored the moderating role of social support with bootstrap techniques. Independent of clinical history, high well-being was associated with better health status, higher social support, more frequent use of cognitive reappraisal and emotional processing.Results: We found a main contribution of emotional processing to well-being (OR = 2.12, 95% CI = 1.09-5.37). The interaction between low suppression and high social support was significant (OR = .40, 95% CI = .13-.79). Probabilities for high well-being were 96% when expressive suppression was low and social support was high. Results suggest approaching one's own emotions may contribute to well-being in long-term childhood cancer survivors.Clinical implications: Combining curbing emotional suppression with promoting supportive social environment could be a promising target for future supportive care interventions in survivors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".