Impact of restricted family presence during the COVID-19 pandemic on critically ill patients, families, and critical care clinicians: a qualitative systematic review
Bibliographic record
Abstract
BACKGROUND: We aimed to synthesize the qualitative evidence on the impacts of COVID-19-related restricted family presence policies from the perspective of patients, families, and healthcare professionals from neonatal (NICU), pediatric (PICU), or adult ICUs. METHODS: We searched MEDLINE, EMBASE, Cochrane Databases of Reviews and Clinical Trials, CINAHL, Scopus, PsycINFO, and Web of Science. Two researchers independently reviewed titles/abstracts and full-text articles for inclusion. Thematic analysis was completed following appraising article quality and assessing confidence in the individual review findings using standardized tools. RESULTS: We synthesized 54 findings from 184 studies, revealing the impacts of these policies in children and adults on: (1) Family integrated care and patient and family-centered care (e.g., disruption to breastfeeding/kangaroo care, dehumanizing of patients); (2) Patients, families, and healthcare professionals (e.g., negative mental health consequences, moral distress); (3) Support systems (e.g., loss of support from friends/families); and (4) Relationships (e.g., loss of essential bonding with infant, struggle to develop trust). Strategies to mitigate these impacts are reported. CONCLUSION: This review highlights the multifaceted impacts of restricted visitation policies across distinct care settings and strategies to mitigate the harmful effects of these policies and guide the creation of compassionate family presence policies in future health crises. REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=290263 .
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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 | 0.051 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".