Supporting parents as essential care partners in neonatal units during the SARS‐CoV‐2 pandemic
Bibliographic record
Abstract
AIM: To review the evidence on safety of maintaining family integrated care practices and the effects of restricting parental participation in neonatal care during the SARS-CoV-2 pandemic. METHODS: MEDLINE, EMBASE, PsycINFO and CINAHL databases were searched from inception to the 14th of October 2020. Records were included if they reported scientific, empirical research (qualitative, quantitative or mixed methods) on the effects of restricting or promoting family integrated care practices for parents of hospitalised neonates during the SARS-CoV-2 pandemic. Two authors independently screened abstracts, appraised study quality and extracted study and outcome data. RESULTS: We retrieved 803 publications and assessed 75 full-text articles. Seven studies were included, reporting data on 854 healthcare professionals, 442 parents, 364 neonates and 26 other family members, within 286 neonatal units globally. The pandemic response resulted in significant changes in neonatal unit policies and restricting parents' access and participation in neonatal care. Breastfeeding, parental bonding, participation in caregiving, parental mental health and staff stress were negatively impacted. CONCLUSION: This review highlights that SARS-CoV-2 pandemic-related hospital restrictions had adverse effects on care delivery and outcomes for neonates, families and staff. Recommendations for restoring essential family integrated care practices are discussed.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".