Trajectories of emotional well-being in mothers of adolescents and adults with autism.
Bibliographic record
Abstract
Raising an adolescent or adult child with a developmental disability confers exceptional caregiving challenges on parents. We examined trajectories of 2 indicators of emotional well-being (depressive symptoms and anxiety) in a sample of primarily Caucasian mothers (N = 379; M age = 51.22 years at Time 1) of adolescent and adult children with an autism spectrum disorder (ASD; M age = 21.91 years at Time 1, 73.2% male). We also investigated within-person associations of child context time-varying covariates (autism symptoms, behavior problems, residential status) and maternal context time-varying covariates (social support network size and stressful family events) with the trajectories of emotional well-being. Data were collected on 5 occasions across a 10-year period. Average patterns of stable (depressive symptoms) and improved (anxiety) emotional well-being were evident, and well-being trajectories were sensitive to fluctuations in both child and maternal context variables. On occasions when behavior problems were higher, depressive symptoms and anxiety were higher. On occasions after which the grown child moved out of the family home, anxiety was lower. Anxiety was higher on occasions when social support networks were smaller and when more stressful life events were experienced. These results have implications for midlife and aging families of children with an ASD and those who provide services to these families.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".