Impact of Parent-Targeted eHealth on Parent and Infant Health Outcomes
Bibliographic record
Abstract
Improved communication, education, and parental involvement in infant care have been demonstrated to enhance parental well-being and neonatal health outcomes. eHealth has the potential to increase parental presence in the neonatal intensive care unit (NICU). There has been no synthesized review on the direct impact of eHealth use on parental and neonatal health outcomes. The aim of this scoping review is to explore eHealth utilization by families of high-risk newborn infants in the NICU and/or postdischarge on health outcomes. PubMed, CINAHL, and EMBASE were searched from 1980 to October 2015 using key terms for "neonatal," "parents," "eHealth," and "patient education." Criteria of peer-reviewed empirical studies published in English, targeting parents of NICU infants regardless of diagnosis, and eHealth utilization during NICU stay or postdischarge yielded 2218 studies. Extracted data were synthesized using thematic content analysis. Ten studies met inclusion, and 5 themes emerged: usability and feasibility, parental perceived benefits, infant's hospital length of stay, knowledge uptake, and predictors of variations in use. eHealth utilization was found to be desired by parents and promotes positive change in parental experience in the NICU. Actual and perceived benefits of eHealth for parents included ease of use, higher confidence in infant care, satisfaction, and knowledge uptake.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".