Impact of Restricted Visitation Policies during the First Wave of the COVID-19 Pandemic on Communication between Critically Ill Patients, Families, and Clinicians: A Qualitative Interview Study
Bibliographic record
Abstract
Abstract Rationale Restricted visitation policies during the first wave of the coronavirus disease (COVID-19) pandemic have had a major impact on the ways that intensive care unit (ICU) clinicians communicated with patients and their families, requiring the use of innovative strategies to adapt to new communication structures. Objectives The purpose of this study is to describe the impact of restricted visitation policies on communication and to identify strategies that could be used to facilitate better communication within Canadian ICUs from the perspective of those affected. Methods We conducted semistructured individual interviews with critically ill patients, their families, and clinicians from 23 Canadian ICUs during the first wave of the COVID-19 pandemic between July 2020 and October 2020. We used inductive thematic analysis to identify relevant themes and subthemes. Results Forty-one interviews were conducted with 3 patients, 8 family members, 17 nurses, and 13 physicians. Five themes were identified from the analysis: 1) patient and family psychosocial and information needs; 2) communication tools; 3) quality of communication; 4) changing roles and responsibilities of patients and nurses/physicians; and 5) facilitators or barriers to implementing alternative communication. Participants identified strategies to leverage new videoconference technology and communication structures to preserve the quality of communication. Conclusions Our study identified challenges and opportunities related to communication between critically ill patients, families, and ICU clinicians due to the restricted hospital visitation policies during the first wave of the COVID-19 pandemic. The use of videoconference technology and changes to communication structure were important strategies to facilitate effective communication within the ICU.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".