A guided participation nursing intervention to theraupeutic positioning and care (GP_Posit) for mothers of preterm infants: protocol of a pilot randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: In the NICU, interventions intended to enhance maternal sensitivity are indicated in order to optimize preterm infant development and long-term mother-infant attachment. A novel nursing intervention was developed following a theory-oriented methodology and is based upon the guided participation theory for mothers to participate in their preterm infant's therapeutic POSITioning and care (GP_Posit). The primary objective of this study is to evaluate the feasibility and acceptability of (i) the study design; and (ii) the experimental GP_Posit nursing intervention during NICU hospitalization. The secondary objective is to estimate the preliminary effects of GP_Posit on maternal and preterm infant outcomes. METHODS: A pilot parallel-group randomized clinical trial (RCT) was designed where mother-preterm infant dyads are being recruited and randomized to a control group (usual care) or experimental group (GP_Posit intervention). Data collection includes feasibility and acceptability data as well as preliminary effects on maternal sensitivity and infant neurodevelopment. Ethical approval from the University Hospital ethical board was obtained in January 2018 (2017-1540). DISCUSSION: Data collection for this pilot study is expected to end in 2020. Results of this pilot study will inform about the feasibility and acceptability of the study design and GP_Posit intervention, a nursing intervention having the potential to favor maternal sensitivity and infant neurodevelopment in the NICU and guide the elaboration of a large-scale RCT. TRIAL REGISTRATION: clinicaltrial.gov, NCT03677752. Registered 19 September 2018.
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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 | 0.033 | 0.034 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.049 | 0.007 |
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".