Predictors of depressive symptoms in primary caregivers of young children with or at risk for developmental delay
Bibliographic record
Abstract
BACKGROUND: Despite extensive research with families raising children with or at risk for developmental delay (DD), it is not clear whether primary caregivers of these children are at increased risk for depressive symptoms. Discrepant findings in the literature may be owing to heterogeneity of child problems. More research is needed on child, parent and family variables that may increase risk for, or resilience to, caregiver depressive symptoms. Some studies have found that parental resources (e.g. social support and coping strategies) may buffer the effects of parental distress, while other studies have highlighted the role of parental self-efficacy. METHODS: We examined Beck Depression Inventory (BDI) scores in 178 primary caregivers (mainly biological mothers) who had 2-year-old children with or at risk for DD owing to: (a) low birthweight, prematurity or multiple birth (n = 58), (b) other known reasons (e.g. Down syndrome, spina bifida) (n = 67), or (c) unknown reasons (n = 69). RESULTS: We found that 20% (n = 35) of the caregivers scored above the BDI clinical cut-off for depression. Analysis of variance revealed that caregivers with elevated BDI scores had higher child behaviour problem and escape-avoidance coping scores, and lower social support and parent self-efficacy, compared with caregivers without depressive symptoms. Caregivers with children who had DD for unknown reasons had higher BDI scores than caregivers of the other two groups of children. Regression analyses showed that child behaviour problems, escape-avoidance coping strategies and social support predicted caregiver BDI scores, but caregiver self-efficacy only did so when entered independently of social support. Only social support mediated and (marginally) moderated the relationship between child behaviour problems and caregiver depressive symptoms. CONCLUSIONS: These findings suggest that early intervention programmes should carefully consider the interaction of child characteristics (e.g. Diagnosis and behaviour problems), caregiver resources (e.g. coping strategies and social support), and parental mental health and mood when planning and tailoring services for families of children with or at risk for DD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".