Virtual Family-Centered Rounds During the COVID-19 Pandemic – Technology Usability Analysis
Bibliographic record
Abstract
Family-centered rounds (FCR) are multidisciplinary rounds, involving patients and caregivers with the aim of shared decision making in medical care planning. In response to the COVID-19 pandemic, a tertiary care pediatric hospital re-engineered the in-person FCR process used by inpatient Pediatric Medicine teams implemented virtual family-centered rounds (vFCR). As part of a mixed methods study evaluating vFCR, naturalistic observation was used to evaluate the usability of vFCR technology. Functional and user requirements were assessed and confirmed through observation of interactions with technology intended to support vFCR. The duration of individual patient rounds and transition time between patients was also captured. Technology interactions were assessed in terms of what worked (successful interactions) and what did not work (usability issues and errors). Neilsen and Norman’s (1994) usability heuristics were used to support the evaluation and explanation of findings. While naturalistic observation yielded clear results in terms of effectiveness and efficiency, user satisfaction was not formally examined. The identified usability requirements and key characteristics for ease of use and adoption of vFCR identified in this study can be used by other hospitals looking to implement or improve inpatient virtual care technology usability.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".