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Record W4401430969 · doi:10.1016/j.jnn.2024.06.010

NICU virtual rounds: Feasibility and acceptability of enhanced access for families and care providers

2024· article· en· W4401430969 on OpenAlexafffund
Nadeana Norris, Arlene Jiang, Helen McCord, Brianna Hughes, Lisa DeWolfe, Marsha Campbell‐Yeo

Bibliographic record

VenueJournal of Neonatal Nursing · 2024
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenDalhousie University
FundersCisco Systems CanadaIWK Health CentreAtlantic Canada Opportunities Agency
KeywordsNeonatal intensive care unitVideoconferencingFamily centered careMedicineNursingHealth carePediatricsComputer scienceMultimedia

Abstract

fetched live from OpenAlex

To evaluate the feasibility and acceptability of virtual medical rounds in the neonatal intensive care unit (NICU) aimed to enhance access for families and care providers. Families of infants requiring neonatal intensive care in a tertiary level NICU and healthcare providers (HCPs) who attended daily virtual medical rounds via a secure password protected video conferencing system were offered to complete a survey to evaluate their satisfaction with communication and care delivered via a virtual modality. A total of 176 surveys were completed during the evaluation period, 40 family members and 136 HCPs. The median levels of satisfaction were 9.5 (range 3–10) for family members and 9 (range 1–10) for HCPs. Our findings support the use of virtual rounds in the NICU. Further study is warranted regarding family and care provider acceptance, impact and the use of virtual rounds outside the COVID-19 pandemic restrictions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.437
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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