Does family-centred neonatal discharge planning reduce healthcare usage? A before and after study in South West England
Bibliographic record
Abstract
OBJECTIVE: To implement parent-oriented discharge planning (Train-to-Home) for preterm infants in neonatal care. DESIGN: Before and after study, investigating the effects of the intervention during two 11-month periods before and after implementation. SETTING: Four local neonatal units (LNUs) in South West England. PARTICIPANTS: Infants without major anomalies born at 27-33 weeks' gestation admitted to participating units, and their parents. TRAIN-TO-HOME INTERVENTION: A family-centred discharge package to increase parents' involvement and understanding of their baby's needs, comprising a train graphic and supporting care pathways to facilitate parents' understanding of their baby's progress and physiological maturation, combined with improved estimation of the likely discharge date. MAIN OUTCOME MEASURES: Perceived Maternal Parenting Self-Efficacy (PMP S-E) scores, infant length of stay (LOS) and healthcare utilisation for 8 weeks following discharge. RESULTS: Parents reported that the Train-to-Home improved understanding of their baby's progress and their preparedness for discharge. Despite a lack of change in PMP S-E scores with the intervention, the number of post-discharge visits to emergency departments (EDs) fell from 31 to 20 (p<0.05), with a significant reduction in associated healthcare costs (£3400 to £2200; p<0.05) after discharge. In both study phases, over 50% of infants went home more than 3 weeks before their estimated date of delivery (EDD), though no reduction in LOS occurred. CONCLUSIONS: Despite the lack of measurable effect on the parental self-efficacy scores, the reduction in ED attendances and associated costs supports the potential value of this approach.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".