Enhancing Quality of Life and Reducing Anxiety in Children with Leukemia Through Social Support: A Pilot Study
Bibliographic record
Abstract
Leukemia is the most common pediatric cancer and remains a leading cause of mortality in children under 15, with an estimated global incidence of approximately 400,000 cases annually among children and adolescents. Despite advancements in treatment improving survival rates, the disease and its prolonged therapeutic process impose significant physical (e.g., pain, fatigue) and psychological (e.g., anxiety, hopelessness, depression) burdens. Additionally, children with leukemia often face social and academic challenges, including difficulties with social support, adaptation, self-esteem, and educational attainment. The existing literature highlights the need for targeted interventions to address these psychological difficulties and provide holistic support. This study aimed to enhance the emotional well-being and resilience of children with leukemia by fostering a sense of solidarity and self-efficacy through mentorship from leukemia survivors. Thirteen participants (8 girls, 5 boys; mean age = 10 years) from the eastern provinces of the country engaged in structured online sessions over three months with mentors who had successfully recovered from leukemia. These mentors provided both motivational and academic support. Findings revealed statistically significant improvements in participants’ trait anxiety levels (p = 0.029), overall quality of life (p = 0.007), and coping skills related to cancer (p = 0.005). These results suggest that structured mentorship programs can positively impact the psychological and social well-being of children with leukemia. By fostering social connectedness and alleviating motivational challenges, such initiatives may contribute to improved long-term psychological health, underscoring the potential of mentorship-based interventions as a valuable component of comprehensive care strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".