Use of Video Technology in End-of-Life Care for Hospitalized Patients During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Infection control protocols, including visitor restrictions, implemented during the COVID-19 pandemic threatened the ability to provide compassionate, family-centered care to patients dying in the hospital. In response, clinicians used videoconferencing technology to facilitate conversations between patients and their families. OBJECTIVES: To understand clinicians' perspectives on using videoconferencing technology to adapt to pandemic policies when caring for dying patients. METHODS: A qualitative descriptive study was conducted with 45 clinicians who provided end-of-life care to patients in 3 acute care units at an academically affiliated urban hospital in Canada during the first wave of the pandemic (March 2020-July 2020). A 3-step approach to conventional content analysis was used to code interview transcripts and construct overarching themes. RESULTS: Clinicians used videoconferencing technology to try to bridge gaps in end-of-life care by facilitating connections with family. Many benefits ensued, but there were also some drawbacks. Despite the opportunity for connection offered by virtual visits, participants noted concerns about equitable access to videoconferencing technology and authenticity of technology-assisted interactions. Participants also offered recommendations for future use of videoconferencing technology both during and beyond the pandemic. CONCLUSIONS: Clinician experiences can be used to inform policies and practices for using videoconferencing technology to provide high-quality end-of-life care in the future, including during public health crises.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".