The Impact of Restrictive Family Presence Policies in Response to COVID-19 on Family Integrated Care in the NICU: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVES: To conduct a needs assessment with families and their healthcare team to understand the impact of restrictive family presence policies in the neonatal intensive care unit (NICU) in response to COVID-19. BACKGROUND: In response to the COVID-19 pandemic, significant restrictive family presence policies were instituted in most NICUs globally intended to protect infants, families, and HCPs. However, knowledge on the impact of the stress of the pandemic and policies restricting family presence in the NICU on vulnerable neonates and their families remains limited. METHODS: Individuals were eligible to participate if they were a caregiver of an infant requiring NICU care or a healthcare provider (HCP) in the NICU after March 1, 2020. Semi-structured interviews were conducted using a virtual communication platform, and transcripts were analyzed using inductive thematic qualitative content analysis. RESULTS: Twenty-three participants were interviewed (12 families and 11 HCPs). Three themes emerged: (1) successes (family-integrated care, use of technology), (2) challenges (lack of standardized messaging and family engagement, impact on parental wellbeing, institutional barriers, and virtual care), and (3) moving forward (responsive and supportive leadership). CONCLUSIONS: Our findings highlight the significant impact of family restrictions on the mental well-being of families, physical closeness with parents, and empathetic stress to HCPs. Further study of potential long-term impact is warranted.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".