Predictors of distress and well‐being in parents of young children with developmental delays and disabilities: the importance of parent perceptions
Bibliographic record
Abstract
BACKGROUND: Moving from family-centred to child-centred models of service delivery can be stressful for parents as their young children with developmental delays and disabilities transition into school. The purpose of this paper was to explore and compare predictors of both distress and well-being in parents during this transition period. METHODS: A sample of 155 mothers of 113 boys and 42 girls participated in the study. The mean age of the children was 4.9 years and their diagnoses included autism spectrum disorder (52%); unspecified intellectual disability/developmental delay (26%); Down syndrome (12%); other genetic conditions (4%) and other diagnoses (6%). Participants completed surveys primarily online focusing on child characteristics, family resources, parent coping strategies, parental distress and positive gain. RESULTS: Multiple regression analyses were conducted to determine predictors of parent reported distress and positive gain. Parent coping variables were the strongest predictors of both positive gain and parental distress, with reframing emerging as a predictor of positive gain and parent empowerment emerging as a predictor of both greater positive gain and lower parental distress. CONCLUSIONS: The results of this study highlight not only the importance of including positive as well as negative outcomes in research with parents but also the importance of including parent characteristics such as coping strategies (e.g. reframing and empowerment/self-efficacy) as potential predictors of outcome in such studies.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".