Relationship of autistic children’s self-care performance with coping and quality of life in mothers of autistic children
Bibliographic record
Abstract
Introduction: Inability in self-care in children with autism can severely affect different aspects of mothers' lives, especially coping and quality of life.The aim of this study was to evaluate the relationship between autistic children's self-care performance and mothers' coping and quality of life.Material and methods: This was a cross-sectional study.110 mothers and their autistic children were selected via convenience sampling.Three questionnaires were completed by participants.Data were analyzed by SPSS software version 16.Results: There was a strong and direct correlation between self-care performance and coping of mothers and mothers' quality of life (p < 0.001).The variables of coping, quality of life, autistic child's age and gender, number of siblings, level of function, mothers' education and job predicted approximately 70.65% of self-care performance variation.Conclusions: Children with autism had fairly good self-care performance, which had a positive effect on adaptation and consequently quality of life of their mothers.Self-care performance promotes the coping and quality of life in mothers of children with autism.Therefore, health officials and policymakers are recommended to use the findings of this study to increase coping and quality of life in these mothers, improve their self-care performance, and pay attention to this point in clinical practice.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".