Public health measures associated with the COVID-19 pandemic: Mothers' perceptions of emotional and physical closeness with their preterm infant before, during, and after the NICU stay
Bibliographic record
Abstract
BACKGROUND: Public health measures during the COVID-19 pandemic have been shown to affect parents' physical and emotional closeness with their preterm infant in the NICU. However, no study has explored the effects of these restrictions on new mothers' perinatal trajectory. AIM: To explore mothers' perceptions regarding the impact of public health measures associated with the COVID-19 pandemic on their emotional and physical closeness to their preterm infant before, during, and after their NICU hospitalization. METHODS: This qualitative descriptive study included 14 mothers who gave birth to a preterm infant during the COVID-19 pandemic. Mothers participated in semi-structured Zoom interviews conducted between May 2021 and January 2022. RESULTS: Analysis of the mothers' narratives revealed that COVID-19 restrictions affected emotional and physical closeness throughout their perinatal experience. The main theme identified in mothers' accounts of the pregnancy period was "inconsistency and ignorance". For the childbirth period, the main theme was "loneliness and disconnected contact". During hospitalization, the emerging theme was "missed opportunities for physical and emotional closeness". In the post-hospitalization period, mothers described the theme "connecting more versus struggling to connect due to poor mental health". CONCLUSION: According to mothers, public health measures affected their emotional and physical bond with their infants before, during, and after their NICU stay. In the event of another pandemic, it would be crucial to reassess the implemented public health measures and provide support to parents through their entire perinatal experience.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".