Daily stress, coping, and well-being in parents of children with autism: A multilevel modeling approach.
Bibliographic record
Abstract
This study used a repeated daily measurement design to examine the direct and moderating effects of coping on daily psychological distress and well-being in parents of children with Autism Spectrum Disorders (ASD). Twice weekly over a 12-week period, 93 parents provided reports of their daily stress, coping responses, and end-of-day mood. Multilevel modeling analyses identified 5 coping responses (e.g., seeking support, positive reframing) that predicted increased daily positive mood and 4 (e.g., escape, withdrawal) that were associated with decreased positive mood. Similarly, 2 coping responses were associated with decreased daily negative mood and 5 predicted increased negative mood. The moderating effects of gender and the 11 coping responses were also examined. Gender did not moderate the daily coping?mood relationship, however 3 coping responses (emotional regulation, social support, and worrying) were found to moderate the daily stress?mood relationship. Additionally, ASD symptomatology, and time since an ASD diagnosis were not found to predict daily parental mood. This study is perhaps the first to identify coping responses that enhance daily well-being and mitigate daily distress in parents of children with ASD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".