Effects of early intervention on the mental wellbeing and caregiving practices of preterm infant caregivers
Bibliographic record
Abstract
BACKGROUND: The rising incidence of preterm births worldwide presents a pressing public health challenge, affecting both infants and their preterm caregivers. Early Intervention (EI) programs aim to mitigate the negative impacts associated with preterm births on the physical, cognitive, and psychological health of both infants and their caregivers by providing personalized parental support and developmental monitoring. This study addressed the gap in research evaluating the long-term effects of community-based EI programs on the holistic coping mechanisms of families, encompassing mental wellbeing, caregiving competencies, and the transition process from hospital to home care. METHODS: This study evaluated the long-term effects of a community-based EI program (in-home only) and the added benefits of earlier Hospital-to-Home (H2H) support, focusing on preterm caregivers' mental wellbeing and caregiving practices. RESULTS: The findings highlight the extended benefits of EI, demonstrating that program duration and intensity significantly benefit families dealing with neonatal intensive care unit stays. For infants requiring extensive medical intervention, EI support markedly enhances caregiver mental wellbeing. Additionally, interventions initiated earlier yield a more substantial positive effect on preterm caregivers' mental wellbeing after discharge compared to those that commence later. CONCLUSION: These findings suggest the potential role of community-based EI programs and the benefits of supporting preterm caregivers before transitioning home. The study calls for future research to explore the effects of various EI program components to identify which interventions are most effective for addressing preterm infants' specific developmental challenges.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".