Innovative virtual care delivery in a Canadian paediatric tertiary-care centre
Bibliographic record
Abstract
Health care systems and providers have rapidly adapted to virtual care delivery during this unprecedented time. Clinical programs initiated a variety of virtual care delivery models to maintain access to care, preserve personal protective equipment, and minimize infectious disease spread. Herein, we first describe the context within paediatric health delivery during the COVID-19 pandemic in Canada that fueled the rise of virtual care delivery. We then summarize the development, implementation, and beneficial impact of the innovative virtual care delivery programs currently in use at Children's Hospital of Eastern Ontario (CHEO) for both inpatient and outpatient care, specifically in our ambulatory clinics, emergency department, and mental health program. We highlight the transferable unique ways CHEO has integrated virtual care delivery through our governance structure, stakeholder engagement including patient, caregivers and health care providers and staff, development, and use of eHealth tools and novel approaches for patient care requiring physical assessment. We conclude with our vision for the future of virtual care, one component of paediatric care delivery in the post-COVID-19 era, which requires a common framework for virtual care evaluation. Importantly, rapid implementation of a primarily virtual care model at CHEO sustained high volume quality paediatric care. We believe many of these programs should and will remain in the post-pandemic era. A comprehensive, unified approach to evaluation is essential to yield meaningful results that inform sustainable care delivery models that integrate virtual care, and ultimately help ensure the best health outcomes for our patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".