Do Clinical And Psychosocial Factors Affect Health-Related Quality of Life in Adolescents with Chronic Diseases?
Bibliographic record
Abstract
Living with a chronic disease in adolescence can have an impact on the perception of Health-related Quality of Life (HRQoL). Facing the increasing relevance of psychosocial dimensions and also considering the interaction with clinical variables, this study aimed to measure the impact of clinical and psychosocial factors (separated and combined) on adolescent’s reported HRQoL.A cross-sectional study was conducted in a clinical population of 135 adolescents with chronic diseases (n=70 boys), average age: 14±1.5 years old. Through a self-reported questionnaire, HRQoL (KIDSCREEN-10), socio-demographic, clinical variables (diagnostic; time of diagnosis; self-perceived pain; disease severity proxy; disease-related medication intake/use of special equipment), and psychosocial variables (psychosomatic health; resilience; self-regulation; social support) were assessed.Separately, clinical and psychosocial variables showed a significant impact in HRQoL, 27.9% and 62.4%, respectively. Once combined, the previously identified variables had a significant impact (64.2%), but a different contribution from clinical and psychosocial variables was revealed: when first entering the clinical variables (model 1) the variance only reaches 30%, and much more from psychosocial variables seems to explain the total (64.2%); inversely, when first integrating psychosocial variables (model 2), the clinical ones added a small significance to the model (0.6%).The present study underlined the association of clinical (“disease-related”) and psychosocial (“non-disease-related”) factors on HRQoL. Furthermore, it reinforced the need to focus more on psychosocial dimensions, highlighted the potential role of psychosomatic health, resilience, self-regulation and social support. It can be suggested that the identification of impaired psychosocial domains may help professionals to better plan, and achieve effective interventions of psychosocial care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".