The evaluation of family quality of life of children with Autism Spectrum Disorder and Attention Deficit Hyperactive Disorder.
Bibliographic record
Abstract
The Quality of Life (QoL) represents a dimension of the overall status and of the wellbeing that might be influenced by various factors. Researchers suggest that the parents of children with disabilities may be more vulnerable in developing physical or mental issues and that these families have a lower quality of life. Primary objective of the study was to evaluate the QoL of families with Autism Spectrum Disorder (ASD) children as compared with that of families with Attention Deficit Hyperactive Disorder (ADHD) children. The data were collected from 65 children with age ranging between 2 and 14 years, diagnosed with ASD and 49 children diagnosed with ADHD. The Family Quality of Life Survey (FQoL) was used to evaluate the family QoL. The multidimensional model of quality of life explains 48% of the variance of the global evaluation of the family’s quality of life, proportion statistically significant (F (9, 103) = 12.71 p<0.01). Under statistical control of other factors the most important predictors remain family (beta = 0.43, p < 0.001), support from others (beta =- 0.26, p < 0.001), career (beta = 0.23, p < 0.001) and financial status (beta = 0.15, p = 0.04). Parents of children from the ADHD sample believe that family relationships are less important for the family quality of life, have fewer opportunities to improve these relations, a lower initiative which can derive also from the reduced importance they place on this domain.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".