Resilience among family caregivers of children and adolescents followed for diabetes
Bibliographic record
Abstract
Introduction Diabetes is a real public health problem. Some people seem to be more vulnerable to this pathology, especially children and teenagers. To ensure their loved one’s well-being, caregivers can also develop coping strategies. Objectives - Assessing caregivers’ resilience of children and adolescents with diabetes. - Identify factors associated with a high level of resilience. Methods The study was conducted at the University Hospital of Gabès, in the pediatrics and internal medicine departments, as well as in outpatient clinics with caregivers of children and adolescents during the period from March 2024 to May 2024. The sociodemographic and clinical characteristics of caregivers of children and adolescents have been collected using a pre-established form. We used : The resilience scale (CD-RISC) is designed to measure resilience, defined as the ability to cope with traumatic events. The higher the score, the greater the resilience. Results Our sample included 32 caregivers. The mean age was 35.55 years with extremes of 22 and 55 years and the sex ratio (M/F) was 0.6. Among the participants, the majority, 78.1% (n=25), presented symptoms of psychological distress. A high level of stress was perceived by 43.7% of caregivers and satisfaction was noted by 34.4%. The mean resilience score was 59.06 (SD =10.45) with extremes of 39 and 81. We found a significant correlation between a high level of resilience and symptoms of psychological distress (p=0.003), the level of daily stress (p=0.03) and the level of overall satisfaction (p=0.043). Conclusions Our study showed that a high level of resilience is linked to several factors. Interventions targeting stress related to social events should be integrated to increase the caregivers’resilience. Disclosure of Interest None Declared
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".