Virtual family-centered rounds: Adapting inpatient care during COVID-19
Bibliographic record
Abstract
Abstract Background: Family-centered rounds (FCR) are fundamental to pediatric inpatient care. During the COVID-19 pandemic, we aimed to design and implement a virtual family-centered rounds (vFCR) process that allowed continuation of inpatient rounds while following physical distancing guidelines and preserving personal protective equipment (PPE). Methods: A multidisciplinary team developed the vFCR process using a participatory design approach. From April through July 2020, quality improvement methods were used to iteratively evaluate and improve the process. Outcome measures included satisfaction, perceived effectiveness, and perceived usefulness of vFCR. Data were collected via questionnaire distributed to patients, families, staff and medical staff, and analyzed using descriptive statistics and content analysis. Virtual auditors monitored time per patient round and transition time between patients as balancing measures. Results: Seventy-four percent (51/69) of health care providers surveyed and 79% (26/33) of patients and families were satisfied or very satisfied with vFCR. Eighty eight percent (61/69) of health care providers and 88% (29/33) of patients and families felt vFCR were useful. Audits revealed an average vFCR duration of 8.4 min (SD=3.9) for a single patient round and transition time between patients averaged 2.9 min (SD=2.6). Conclusion: Virtual family-centered rounds are an acceptable alternative to in-person FCR in a pandemic scenario, yielding high levels of stakeholder satisfaction and support. We believe vFCR are a useful method to support inpatient rounds, physical distancing, and preservation of PPE that may also be valuable beyond the pandemic. A rigorous process evaluation of vFCR is underway.
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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 | 0.011 | 0.024 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".