Development and Design of a Nursing Intervention Following a Theory and Evidence-Based Approach to Promote Maternal Sensitivity and Preterm Infant Neurodevelopment in the NICU
Bibliographic record
Abstract
Abstract Background: Maternal sensitivity is an important predictor of long-term mother-infant attachment and infant development. Considering the behavioral specificities of preterm infants that may impede the development of maternal sensitivity, it is essential to promote these outcomes soon after a preterm birth. A systematic review showed that current evidence on the effectiveness of parent-infant intervention promoting parental sensitivity in the neonatal intensive care unit (NICU) is of low to very low quality. The aim of this project was to develop and design a novel nursing intervention to enhance maternal sensitivity and preterm infant neurodevelopment in the NICU. Methods: The Medical Research Council’s guidance to develop and evaluate complex health interventions, that is an evidence and theory-based approach, was used for this study. Thus, based on the MRC framework, three main steps were conducted: 1- Identifying existing empirical evidence; 2- Identifying and developing theory; 3- Modeling processes and outcomes.Results: We developed a guided participation intervention for mothers to participate in their preterm infant’s care and positioning (GP_Posit). GP_Posit is based upon the Attachment theory, the Guided participation theory as well as the Synactive theory of development. Conclusion: This novel intervention is being tested in a pilot randomized controlled trial (NCT03677752).
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".