P.093 Implementation of virtual interdisciplinary bedside rounds on an acute stroke unit
Bibliographic record
Abstract
Background: The novel corona virus pandemic presented the Saskatoon Stroke Program with challenges related to patient- and caregiver-centered communication. Keeping all parties informed of a patient’s health status and plan of care in the setting of extreme visitation restrictions was difficult. Virtual interdisciplinary bedside rounds (VIDR) were introduced to enhance communication for stroke patients. Methods: A video conferencing application was adopted by the Saskatchewan Health Authority. Consent to participate was obtained by a social worker. Bedside nurses facilitated patient participation in VIDR on either a tablet or workstation on wheels, while caregivers were able to attend virtually. Each team member accessed the VIDR from an individual device to maintain social distancing. A structured questionnaire has been initiated to capture participant reported experiences and satisfaction with VIDR (data collection ongoing). Results: Most patients and caregivers were amiable to participate in VIDR. Challenges included: accessing appropriate technology for both family and staff members; rural and remote internet reliability; and maintaining a reasonable duration of rounds. There was overwhelming anecdotal positive feedback from participants. Conclusions: We implemented VIDR to enhance communication during the pandemic. Caregivers felt connected to the care team and up-to-date in the plan of 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".