Barriers to, and facilitators of, eHealth utilisation by parents of high-risk newborn infants in the NICU: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Parental presence in the neonatal intensive care unit (NICU) has been demonstrated to enhance infant growth and development, reduce parental anxiety and stress and strengthen parent-infant bonding. Since eHealth technology emerged, research on its utilisation in NICUs has risen substantially. There is some evidence that incorporating such technologies in the NICU can reduce parental stress and enhance parent confidence in caring for their infant.Several countries, including China, restrict parental attendance in NICUs, citing infection control challenges, issues of privacy and confidentiality and perceived additional workload for healthcare professionals. Due to COVID-19 pandemic-related shortages of personal protective equipment and uncertain mode of transmission, many NICUs around the world closed to parental visiting and engagement in neonatal care.There is anecdotal evidence that, given pandemic-related restrictions, eHealth technologies, have increasingly been used in NICUs as a potential substitute for in-person parental presence.However, the constraints and enablers of technologies in these situations have not been exhaustively examined. This scoping review aims to update the literature on eHealth technology utilisation in the NICU and to explore the literature on the challenges and facilitators of eHealth technology implementation to inform future research. METHODS AND ANALYSIS: The five-stage Arksey and O'Malley methodological framework and the Joanna Briggs Institute scoping review methodology will serve as the foundation for this scoping review. Eight databases will be searched for the relevant literature published between January 2000 and August 2022 in either English or Chinese. Grey literature will be manually searched. Data extraction and eligibility screening will be carried out by two impartial reviewers. There will be periods of both quantitative and qualitative analysis. ETHICS AND DISSEMINATION: Since all data and information will be taken from publicly accessible literature, ethical approval would not be necessary. A peer-reviewed publication will be published with the results of this scoping review. TRIAL REGISTRATION NUMBER: This scoping review protocol was registered in Open Science Framework and can be found here: https://osf.io/AQV5P/.
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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 | 0.064 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".