eHealth interventions for parents in neonatal intensive care units: a systematic review
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: As technology becomes increasingly more advanced, particularly video technology and interactive learning platforms, some neonatal intensive care units are embracing electronic health (eHealth) technologies to enhance and expand their family-centered care environments. Despite the emergence of eHealth, there has been a lack of systematic evaluation of its effectiveness thus far. OBJECTIVES: To examine the effect of eHealth interventions used in neonatal intensive care units on parent-related and infant outcomes. INCLUSION CRITERIA TYPES OF PARTICIPANTS: This review considered studies that included parents or primary caregivers of infants requiring care in a neonatal intensive care unit. TYPES OF INTERVENTION(S): This review considered studies that evaluated any eHealth interventions in neonatal intensive care units, including education (e.g. web-based platforms, mobile applications); communication (e.g. videos, SMS or text messaging), or a combination of both. Comparators included no eHealth interventions and/or standard care. TYPES OF STUDIES: Experimental and epidemiological study designs including randomized controlled trials, non-randomized controlled trials, quasi-experimental, before and after studies, prospective and retrospective cohort studies, case-control studies, and analytical cross sectional studies were considered. OUTCOMES: This review considered studies that included parent-related outcomes (use and acceptance, stress/anxiety, confidence, financial impact, satisfaction and technical issues) and neonatal outcomes (length of stay, postmenstrual age at discharge, parental presence and visits). SEARCH STRATEGY: A systematic search was undertaken across four databases to retrieve published studies in English from inception to November 18, 2016. METHODOLOGICAL QUALITY: Critical appraisal was undertaken by two independent reviewers using standardized critical appraisal instruments from the Joanna Briggs Institute System for the Unified Management, Assessment and Review of Information (JBI-SUMARI). DATA EXTRACTION: Quantitative data were extracted from included studies independently by two reviewers using the standardized data extraction tool from JBI-SUMARI. DATA SYNTHESIS: A comprehensive meta-analysis for all outcomes was not possible and data has been reported narratively for all outcomes. RESULTS: Eight studies met inclusion criteria and were included in the review. The majority of the studies were low to very low quality. The study design and type of eHealth technology examined varied greatly. There appears to be growing interest in the topic as over half of the included studies were published within the past two years. Primary findings suggest parent acceptance and use of eHealth interventions but an unclear impact on neonatal outcomes, particularly on length of stay, a commonly reported neonatal outcome. Due to the variation in eHealth interventions, and heterogeneity across studies, meta-analysis was not possible. Numerous single studies and small sample sizes limited the degree of adequate strength to determine statistical differences across outcomes. CONCLUSIONS: While heterogeneity across studies precluded meta-analysis, consistent trends across all studies examining parental acceptance of eHealth interventions indicate that parents are willing to accept eHealth interventions as part of their neonatal intensive care, suggesting that the incorporation and evaluation of eHealth interventions in the neonatal intensive care unit setting is warranted. Further high quality studies are needed with larger sample sizes to detect changes in outcomes. As eHealth intervention studies move beyond feasibility and implementation, there is a demand for randomized control trials to examine the effect of eHealth interventions on parent and neonatal outcomes compared to usual care. Future studies should consider reporting of outcomes using standardized measures which would allow comparison across eHealth interventions in subsequent reviews.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it