Development of clinical virtual care pathways to engage and support families requiring neonatal intensive care in response to the COVID-19 pandemic (COVES Study)
Bibliographic record
Abstract
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.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".