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Record W3151455004 · doi:10.1101/2021.03.29.21254567

Development of clinical virtual care pathways to engage and support families requiring neonatal intensive care in response to the COVID-19 pandemic (COVES Study)

2021· preprint· en· W3151455004 on OpenAlexaff
Marsha Campbell‐Yeo, Justine Dol, Brianna Hughes, Holly McCulloch, Amos Hundert, Sarah Foye, Jon Dorling, Jehier Afifi, Tanya Bishop, Rebecca Earle, Annette Elliott Rose, Darlene Inglis, Theresa Kim, Carye Leighton, Sally Loring, Gail MacRae, Andrea Melanson, David C. Simpson, Michael Smit, Leah Whitehead

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsNova Scotia Health AuthorityIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsHealth carePandemicNursingIntensive carePsychologyMedicineCoronavirus disease 2019 (COVID-19)DiseasePolitical science

Abstract

fetched live from OpenAlex

Abstract Background In response to the COVID-19 pandemic, family presence restrictions in neonatal intensive care units (NICU) were enacted to limit disease transmission and protect infants, families, and healthcare providers. The effects of pandemic parental restrictions on providing optimal family integrated neonatal care is unknown. Aim To ensure optimal neonatal care using virtual care pathways to engage and support families in response to parental presence restrictions imposed during the COVID-19 pandemic. The research had two objectives: (1) conduct a needs assessment with families and healthcare providers (HCPs) of infants in the NICU to understand the impact of COVID-19 restrictions; and (2) develop virtual clinical care pathways to meet identified needs. Methods This study used focus groups and individual semi-structured interviews with families and HCPs for the needs assessment and identification of barriers and facilitators, and co-design for the development of the clinical virtual care pathways. For objective 1, content analysis was conducted by two independent reviewers to categorize findings and identify important barriers and facilitators of family-integrated care. For objective 2, an agile, co-design process utilizing expert consensus of a large interdisciplinary team was used to develop the care pathways. Results A total of 23 participants were included in the needs assessment (objective 1): 12 families and 11 HCPs. Themes identified were: (1) the need to maintain and build relationships and support systems; (2) challenges in accessing education and resources to integrate families in care; and (3) lack of standardized, accessible messaging related to COVID-19. For objective 2, we used the themes identified in the needs assessment to co-design three clinical virtual care pathways: (1) building and maintaining relationships between family and healthcare providers; (2) awareness of resources; and (3) standardized COVID-19 messaging. Conclusion Families reported that restrictive parental presence policies affected their mental health, well-being and social support. Families and HCPs reported the restrictions impacted delivery of family integrated care, education, transition to home, and standardized messaging. Clinical care virtual pathways were designed to meet these needs to ensure more equitable family centred care.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0020.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.114
GPT teacher head0.380
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2021
Admission routes1
Has abstractyes

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