Experiences of Mothers of Preterm Infants in the Neonatal Intensive Care Unit During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: The neonatal intensive care unit (NICU) stay following the birth of a preterm infant can be stressful and traumatic for families. During the COVID-19 pandemic, the NICU environment changed precipitously as infection control and visitor restriction measures were implemented. PURPOSE: Our study aimed to examine the impact of the pandemic policies on the experiences of mothers of preterm infants during their stay in the NICU. METHODS: Semistructured interviews were conducted with mothers of preterm infants hospitalized in a Canadian tertiary-level NICU. Informed by interpretive description methodology, interview content was transcribed and analyzed using a thematic analysis approach. The identified themes were validated, clarified, or refined using investigator triangulation. RESULTS: Nine English-speaking mothers, aged 28 to 40 years, were interviewed. Four themes emerged from the analysis of their experiences: (1) disrupted family dynamic, support, and bonding; (2) physical and emotional isolation; (3) negative psychological impact compounded by added concerns, maternal role change, and survival mode mentality; and (4) positive aspects of the pandemic management measures. IMPLICATIONS FOR PRACTICE: During the pandemic, the way that care was provided in the NICU changed. This study helps to explore how neonatal clinicians can foster individual and organizational resilience to keep patients and families at the center of care, even when the healthcare system is under intense stress. IMPLICATIONS FOR RESEARCH: : Our results show that these changes heightened mothers' distress, but also had a modest positive impact. Further research about long-term consequences of pandemic policies on the mother and preterm infant after NICU discharge 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".