Postnatal depressive symptoms in mothers of infants at high risk of cerebral palsy: the role of delayed infant communicative development
Bibliographic record
Abstract
Purpose Recent diagnostic advantages enable detection of cerebral palsy (CP) in infants before five months of age. Parents of children with CP often face mental health problems, but specific knowledge for infancy is needed. In this study, depressive symptoms in mothers of 16-week-old infants and associations with infant development were investigated.Materials and methods This cross-sectional study involves 56 families, 22 high-risk and 34 infants without risk of CP. High-risk-CP was identified following international clinical guidelines. We assessed infant cognitive and language development using the Bayley-III and motor development using the Alberta Infant Motor Scale. Maternal depressive symptoms were self-reported using the Edinburgh Postnatal Depression Scale.Results Mothers of CP high-risk infants were 15.6 times more likely to experience risk of postnatal depression compared to mothers of infants without risk. Additionally, linear regression analyses showed that having an infant at high-risk of CP (β = .359, p = .006) and delayed language development (β = −0.510, p < .001) were associated with increased maternal depressive symptoms.Conclusions We recommend systematic screening of postnatal depressive symptoms following detection of high-risk-CP in infants. Early interventions could include a mother-infant interactional component to support caregivers in interpreting and responding to infant communicative cues.
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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 | 0.003 |
| 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".