Impact of restricted visitation policies during COVID-19 on critically ill adults, their families, critical care clinicians, and decision-makers: a qualitative interview study
Bibliographic record
Abstract
PURPOSE: During the first wave of the COVID-19 pandemic, restricted visitation policies were enacted at acute care facilities to reduce the spread of COVID-19 and conserve personal protective equipment. In this study, we aimed to describe the impact of restricted visitation policies on critically ill patients, families, critical care clinicians, and decision-makers; highlight the challenges faced in translating these policies into practice; and delineate strategies to mitigate their effects. METHOD: A qualitative description design was used. We conducted semistructured interviews with critically ill adult patients and their family members, critical care clinicians, and decision-makers (i.e., policy makers or enforcers) affected by restricted visitation policies. We transcribed semistructured interviews verbatim and analyzed the transcripts using inductive thematic analysis. RESULTS: Three patients, eight family members, 30 clinicians (13 physicians, 17 nurses from 23 Canadian intensive care units [ICUs]), and three decision-makers participated in interviews. Thematic analysis was used to identify five themes: 1) acceptance of restricted visitation (e.g., accepting with concerns); 2) impact of restricted visitation (e.g., ethical challenges, moral distress, patients dying alone, intensified workload); 3) trust in the healthcare system during the pandemic (e.g., mistrust of clinical team); 4) modes of communication (e.g., communication using virtual platforms); and 5) impact of policy implementation on clinical practice (e.g., frequent changes and inconsistent implementation). CONCLUSIONS: Restricted visitation policies across ICUs during the COVID-19 pandemic negatively affected critically ill patients and their families, critical care clinicians, and decision-makers.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".