Perspectives on Communication Technology Use for Alleviating the Impact of COVID-19 on Hospitalized Patients’ Well-Being and Transitions in Care
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic created many challenges for in-patient care including patient isolation and limitations on hospital visitation. Although communication technology, such as video calling or texting, can reduce social isolation, there are challenges for implementation, particularly for older adults. OBJECTIVE/METHODS: This study used a mixed methodology to understand the challenges faced by in-patients and to explore the perspectives of patients, family members, and health care providers (HCPs) regarding the use of communication technology. Surveys and focus groups were used. FINDINGS: Patients who had access to communication technology perceived the COVID-19 pandemic to have more adverse impact on their well-beings but less on hospitalization outcomes, compared to those without. Most HCPs perceived that technology could improve programs offered, connectedness of patients to others, and access to transitions of care supports. Focus groups highlighted challenges with technology infrastructure in hospitals. DISCUSSION: Our study findings may assist efforts in appropriately adopting communication technology to improve the quality of in-patient and transition care.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".