Virtual family-centred rounds support high-quality care in paediatric inpatients: A mixed-methods process evaluation
Bibliographic record
Abstract
Objectives: Virtual care use exploded during the COVID-19 pandemic, but comprehensive evaluations of inpatient virtual care are lacking. This process evaluation of virtual family-centred rounds (vFCR) aimed to determine if vFCR supports high-quality care with comparable effectiveness, usefulness, and acceptability to in-person family-centred rounds. Methods: This mixed-methods evaluation was conducted in a freestanding academic tertiary care children's hospital from May to June 2021. Virtual observations were used to evaluate the effectiveness of vFCR compared to in-person rounds and timing benchmarks. In-person observations were used to collect vFCR technology usability data. Questionnaires were distributed postrounds to understand patient/family and medical team perceptions of vFCR effectiveness, satisfaction, safety, and technology usability. Results: Adherence to the core components of family-centred rounds during vFCR was variable. vFCR timing was consistent with benchmarks despite regular delays. The majority (93%) of survey respondents were satisfied with vFCR, and 67% felt it was important to do FCR virtually during the pandemic for increased safety. Importantly, vFCR was perceived by 97% of medical team members as supporting shared decision-making with patients and families and 78% of patients/families felt like valued partners in their (child's) care. vFCR technology was perceived as easy or very easy to use by 95% of respondents. Conclusion: Virtual family-centred rounds were found to be effective, safe and met with high levels of satisfaction by patients/families and medical team members. The technology was perceived to be easy to learn and use. Rounds efficiency and transition times were identified as opportunities for improvement.
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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 | 0.073 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".