Barriers to and facilitators for supporting patient communication in the adult <scp>ICU</scp> during the <scp>COVID</scp> ‐19 pandemic: A qualitative study
Bibliographic record
Abstract
Abstract Aim To explore barriers to and facilitators for supporting communication with and for patients treated with invasive mechanical ventilation in the intensive care unit during the COVID‐19 pandemic. Design A qualitative descriptive study reported according to the Consolidated Criteria for Reporting Qualitative Research. Methods Adult intensive care unit patients treated with an advanced airway for mechanical ventilation, their family members and healthcare providers (nurses, intensivists and allied health) were recruited for interviews between January and April 2021. Interviews were audio‐recorded, transcribed verbatim and analysed using content analysis methods. Reported communication barriers and facilitators were mapped to the theoretical framework of acceptability to identify potentially modifiable clinical and institutional practices. Results We recruited 29 participants (20 healthcare providers, four patients and five family member). Provider communication barriers included isolation procedures, lack of personal protective equipment and inadequate communication tools/training, which reduced perceived communication frequency and effectiveness. Patients and families reported infrequent proactive provision of communication tools, which contributed to a crisis of unmet needs. Reported facilitators included adequate access to personal protective equipment to mitigate the risk of patient proximity and communication tools/training to improve communication effectiveness. Authentic unit leadership helped to assuage pandemic work stressors and encourage humanistic care. Our analysis indicates low acceptability of existing communication practices during the COVID‐19 pandemic and the importance of leadership to reduce the burden of communication through provision of key necessary resources. Conclusion COVID‐19 conditions have precipitated a communication crisis in the ICU. The results of this study have practice and policy implications and will be used to co‐design a communication intervention for use during and beyond the COVID‐19 pandemic. Impact The study contributes a better understanding of resources necessary to support patient communication. Results apply beyond the pandemic to routine use of infection prevention and control precautions in the intensive care unit.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".