Tailored Psychoeducational Home Interventions for Children with a Chronic Illness: Families’ Experiences
Bibliographic record
Abstract
The quality of life for a child with a chronic illness depends on various factors, including the illness's severity, medical treatments, psychosocial and educational support, resource availability, and community involvement. These biopsychosocial factors become significant when the child receives care at home. This article presents and evaluates a highly personalized support project offered to 40 Sicilian families, consisting of educational, social, and psychological services delivered at the families homes and in their communities. Guided by the Psychosocial Assessment Tool (PAT) and the Functional Psychology framework, the project employed a family-focused approach to healthcare and was based on a continuous dialogue between all stakeholders. The project was evaluated through a qualitative interview with eight families in the Palermo area, which was analyzed using consensual qualitative research. Results revealed families' appreciation of the project and the importance of a professional who listened to their needs, provided a connection with the medical team, and tailored activities inside and outside the home. The ability of professionals to listen and adapt activities to different contexts and needs was crucial for the project's success. We conclude that creating tailored family-level interventions with an educator acting as a liaison with the medical team is a widely acceptable strategy that should be further developed and investigated.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".