Evaluation of family and staff experiences with virtual rounding and bedside presence in a tertiary neonatal intensive care unit during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic-related visitation restrictions started in March 2020 in Alberta, Canada. In the Neonatal Intensive Care Unit, we implemented a Virtual Communications initiative to allow parents to continue to be present with their infants, attend daily rounds, and communicate with the medical team. The purpose of this survey study was to describe our approach and evaluate the experience for families and staff. METHODS: The study surveys consisted of 13-18 questions directed toward understanding staff and family experience with the process and emotional impact using Likert scale and open-ended questions. The study team reviewed results and implemented changes in real time. Analysis was mixed quantitative and qualitative design, with descriptive data organized into themes. RESULTS: Twenty-six surveys were completed by 16 staff (62%) and 10 parents (38%). About 50% to 100% of respondents agreed or strongly agreed with statements addressing the quality and value of the virtual sessions. Staff identified challenges with slow devices and need for awareness and education. Both staff and parents expressed gratitude for the initiative and an overall positive experience. DISCUSSION: Offering Virtual Rounds and Bedside Presence in the Neonatal Intensive Care Unit is a well-received and feasible alternative to in-person presence that allows parents to stay involved and connected to their infants. Families have a better understanding of their babies' clinical status and plans with an overall positive experience.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".