Virtual visiting in intensive care during the COVID-19 pandemic: a qualitative descriptive study with ICU clinicians and non-ICU family team liaison members
Bibliographic record
Abstract
OBJECTIVE: To understand the experiences and perceived benefits of virtual visiting from the perspectives of intensive care unit (ICU)-experienced clinicians and non-ICU-experienced family liaison team members. DESIGN: Qualitative descriptive study. SETTING: Adult intensive care setting across 14 hospitals within the UK National Health Service. PARTICIPANTS: ICU-experienced clinicians and non-ICU-experienced family liaison team members deployed during the first wave of the COVID-19 pandemic. METHODS: Semistructured telephone/video interviews were conducted with ICU clinicians. Analytical themes were developed inductively following a standard thematic approach, using 'family-centred care' and 'sensemaking' as sensitising concepts. RESULTS: We completed 36 interviews, with 17 ICU-experienced clinicians and 19 non-ICU-experienced family liaison team members. In the context of inperson visiting restrictions, virtual visiting offered an alternative conduit to (1) restoring the family unit, (2) facilitating family involvement, and (3) enabling sensemaking for the family. Virtual visits with multiple family members concurrently and with those living in distant geographical locations restored a sense of family unit. Family involvement in rehabilitation, communication and orientation activities, as well as presence at the end of life, highlighted how virtual visiting could contribute to family-centred care. Virtual visits were emotionally challenging for many family members, but also cathartic in helping make sense of their own emotions and experience by visualising their relatives in the ICU. Being able to see and interact with loved ones and their immediate care providers afforded important cues to enable family sensemaking of the ICU experience. CONCLUSIONS: In this UK qualitative study of clinicians using virtual ICU visiting, in the absence of inperson visiting, virtual visiting was perceived positively as an alternative that promoted family-centred care through virtual presence. We anticipate the perceived benefits of virtual visiting may extend to non-pandemic conditions through improved equity and timeliness of family access to the ICU by offering an alternative option alongside inperson visiting.
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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 | 0.007 | 0.016 |
| 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 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".