Effect of Mindfulness Training on Distress Tolerance and Alexithymia in Mothers With Autistic Children
Bibliographic record
Abstract
Background: Autism disorder in children is characterized by problems in social functioning, communication, and the existence of repetitive and stereotyped behaviors. The pervasive and severe disabilities of children with autism are a difficult experience for their parents and families and are often accompanied by a range of challenges for caregivers. Therefore, designing appropriate programs to improve distress tolerance and reduce alexithymia is needed as a priority in health care plans. Mindfulness training is an effective way to teach a variety of skills to mothers of children with autism. Objectives: The aim of this study was to determine the effect of mindfulness training on distress tolerance and alexithymia in mothers with autistic children. Materials & Methods: In this quasi-experimental study, the study population included all mothers of children with autism in Rasht city who referred to Negah-e No Psychological Counseling Center in Guilan Province, Iran, in 2019. From this statistical population, 30 people were selected by convenience sampling and were randomly assigned to the experimental and control groups. The research instruments were Simmons and Gauher Confusion Tolerance Questionnaire (2005) and Toronto Alexithymia Scale (TAS) (1994). Data were analyzed using multivariate analysis of covariance (MANCOVA) by IBM SPSS v. 24. Results: There was a significant difference between the groups in terms of distress tolerance (P<0.001, F=59.45) and alexithymia (P<0.001, F=20.52). Conclusion: Mindfulness training increased distress tolerance and decreased alexithymia in mothers with autistic children.
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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 | 0.001 |
| 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) | 0.001 | 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".