use of digital tablets eases the way to compassionate care during the COVID-19 pandemic
Bibliographic record
Abstract
Objective: A patient communication program was implemented as a response to hospitals visiting restrictive policies during the COVID-19 pandemic. The aim of the program was to facilitate communication between patients and families, mainly through the use of digital tablets; thus program performance was evaluated by selecting the number of calls performed, the average call time, and the percentage of patients that used the program more than once. Methods: A communication service for hospitalized patients who did not have access to a personal electronic device or were unable to use their electronic device was launched at different MUHC hospitals. A dedicated team of re-deployed employees was available to help patients connect with their loved ones using a hospital tablet or telephone. Results: A total of 806 calls were performed between April and November 2020. Eighty one percent of the calls were performed during the non-visitors policy implementation, being video calls preferred over phone calls. The average call time was 15 min, 34% of the patients had a video call with their loved one more than once and 40% of the calls were performed in the intensive care unit. Conclusion: The patient communication program can be described as a new delivery model of compassionate care. It was effective, helped reduce patients’ isolation and met the needs of family members and caregivers during the hospital non-visitors policy directed by the Ministère de la Santé et des Services Sociaux de Québec during the Covid-19 pandemic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".