Mindfulness, Quality of Life, and Resilience among Mothers of Children with ASD: The Mediating Role of Cognitive Emotion Regulation Strategies
Bibliographic record
Abstract
Background: The aim is to investigate the relationships between mindfulness, QoL, and resilience based on the mediating role of Cognitive Emotion Regulation strategies (CERs) data on 110 mothers were collected and analyzed. Methods: A descriptive research with a correlational model in which the path analysis model is used to obtain the relationship between variables was used. Results indicate that Adaptive CER has a direct and significant correlation with QoL, Mindfulness, and Resilience. Non-Adaptive CER has an inverse and significant correlation with QoL, Mindfulness, and Resilience. Results: The results of structural equation modeling indicate that all paths adaptive CERs and non-Adaptive CERs were significant. E.g. the path of mindfulness to adaptive CERs was significant, and to non-adaptive CERs was significant. The path of QoL to adaptive CERs was significant, and to non-Adaptive CERs was significant. The path of resilience to adaptive CERs was significant, and to non-Adaptive CERs was significant. Conclusion: It can be said that when the mother has mental health components, she can provide a rich and healthy environment for the child with ASD to be able to help him/her develop.
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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 | 0.000 |
| 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 | 0.000 |
| 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".