A pilot cohort analytic study of Family Integrated Care in a Canadian neonatal intensive care unit
Bibliographic record
Abstract
BACKGROUND: We have developed a Family Integrated Care (FIC) model for use in a neonatal intensive care unit (NICU) where parents provide most of the care for their infant, while nurses teach and counsel parents. The objective of this pilot prospective cohort analytic study was to explore the feasibility, safety, and potential outcomes of implementing this model in a Canadian NICU. METHODS: Infants born ≤ 35 weeks gestation, receiving continuous positive airway pressure or less respiratory support, with a primary caregiver willing and able to spend ≥ 8 hours a day with their infant were eligible. Families attended daily education sessions and were mentored at the bedside by nurses. The primary outcome was weight gain, as measured by change in z-score for weight 21 days after enrolment. For each enrolled infant, we identified two matched controls from the previous year's clinical database. Differences in weight gain between the two groups were analyzed using a linear mixed effects multivariable regression model. We also measured parental stress levels using the Parental Stress Survey: NICU, and interviewed parents and nurses regarding their experiences with FIC. RESULTS: This study included 42 mothers and their infants. Of the enrolled infants, matched control data were available for 31 who completed the study. The rate of change in weight gain was significantly higher in FIC infants compared with control infants (p < 0.05). There was also a significant increase in the incidence of breastfeeding at discharge (82.1 vs. 45.5%, p < 0.05). The mean Parental Stress Survey: NICU score for FIC mothers was 3.06 ± 0.12 at enrolment, which decreased significantly to 2.30 ± 0.13 at discharge (p < 0.05). Feedback from the parents and nurses indicated that FIC was feasible and appropriately implemented. CONCLUSIONS: This study suggests that the FIC model is feasible and safe in a Canadian healthcare setting and results in improved weight gain among preterm infants. In addition, this innovation has the potential to improve other short and long-term infant and family outcomes. A multi-centre randomized controlled trial is needed to further evaluate the efficacy of FIC in the Canadian context.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".