Effect of Early Intervention to Promote Mother - Infant Interaction and Maternal Sensitivity in Japan: A Parenting Support Program based on Infant Mental Health.
Bibliographic record
Abstract
This study investigated the effects of the Japanese Early Promotion Program (JEPP), which is based on the Infant Mental Health (IMH) program. The JEPP aims to promote mother-infant interactions by enhancing the mother's ability to respond appropriately her child. Mothers in the JEPP group (n = 15) received support from IMH nurses in a pediatric clinic until their infants reached 12 months of age. The nurses provided positive feedback that emphasized strength of parenting, and assisted the mothers in understanding the construct of their infants. Mother-infant interactions and mother's mental health status were assessed at intake (1-3 months), and at 6, 9, and 12 months of infants' age. The JEPP group data were compared with cross-sectional data of the control group (n = 120). Although JEPP dyads were not found to be significantly different from the control group in general dyadic synchrony, both before and after intervention, JEPP mothers significantly improved their ability to understand their infant's cues and to respond promptly. In the JEPP group, unresponsiveness to infants was reduced in mothers, while infants showed reduced passiveness and enhanced responsiveness to the mother. Furthermore, the intervention reduced the mothers' parenting stress and negative emotions, thereby enhancing their self-esteem.
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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 | 0.001 |
| 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 | 0.001 |
| 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".