NeoConnect
Bibliographic record
Abstract
BACKGROUND: Familial involvement in the neonatal intensive care unit (NICU) reduces parental stress and strengthens parental-infant bonding. However, parents often face barriers to in-person visitation. The coronavirus disease-2019 COVID-19 pandemic has exacerbated limitations to parental bedside presence. OBJECTIVE: To design, implement, and evaluate a technology-based program to connect NICU babies with their families during the COVID-19 pandemic. METHODS: We created NeoConnect at our level IV NICU, which included parental audio recordings and video chats between parents and their babies. Parental and NICU staff input on NeoConnect was gathered via preimplementation surveys. Inaugural families and staff members completed a postparticipation survey. RESULTS: Prior to implementation, all parents who were surveyed (n = 24) wished they could be more involved in their baby's care. In the first 3 months of NeoConnect, 48 families participated in the audio recording project and 14 families participated in the video chat initiative. Following implementation, 85% of surveyed staff (28/33) reported that the patients became calmer when listening to their parents' recorded voice and 100% of surveyed parents (6/6) reported that video chats reduced their stress level. CONCLUSION: Harnessing technology as a tool to increase parental involvement in the NICU is feasible and beneficial for NICU patients and their families.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.177 | 0.035 |
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".