Video calls during the COVID-19 pandemic: A bridge for patients, families, and respiratory therapists.
Bibliographic record
Abstract
BACKGROUND: In response to COVID-19 pandemic outbreak, healthcare procedures and modalities have changed considerably. Video calls may supply valid support for health care professionals, contributing to maintaining the communication between hospitalized patients and their families. The present study aimed to observe the experience of a group of respiratory therapists who conducted video calls between patients and their families in a COVID-19 unit. METHOD: Semistructured interviews (20-30 minutes duration) with the respiratory therapists who conducted the video calls in a COVID-19 unit in the North of Italy were performed. Participants joined the study voluntarily and, after receiving the informed consent, the interviews were audio-recorded. To better understand healthcare professionals' experience, the Interpretive Description methodology was adopted. RESULTS: Seven respiratory therapists (three males, four females; mean age 45.1 ± 14.4) were recruited. Sixteen codes emerged from the analysis, constituting five primary themes: Contact, Impact, Challenges, Centering, and Future. CONCLUSIONS: The findings shed light on the usability of video calls during this emergency period and their potential for future implementation. Specifically, this type of call may bridge the distances in such periods of care and help professionals to emotionally support patients and caregivers. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".