A pediatric virtual care evaluation framework and its evolution using consensus methods
Bibliographic record
Abstract
BACKGROUND: The use of virtual care has increased dramatically in response to the COVID-19 pandemic, yet evidence is lacking regarding the impact of virtual care on patient outcomes, particularly in pediatrics. A standardized evaluation approach is required to support the integration of virtual care into pediatric health care delivery programs. The objective of this work was to develop a comprehensive and structured framework for pediatric virtual care evaluation. This framework is intended to engage and guide care providers, health centres, and stakeholders towards the development of a standardized approach to the evaluation of pediatric virtual care. METHODS: We brought together a diverse multidisciplinary team, including pediatric clinicians, researchers, digital health leads and analysts, program leaders, a human factors engineer, a family advisor and our manager of health equity and diversity. The team reviewed the literature, including published evaluation frameworks, and used a consensus-based method to develop a virtual care evaluation framework applicable to a broad spectrum of pediatric virtual care programs. We used an iterative process to develop framework components, including domains and sub-domains, examples of evaluation questions, measures, and data sources. Team members met repeatedly over seven months to generate and provide feedback on all components of the framework, making revision as needed until consensus was reached. The framework was then applied to an existing virtual care program. RESULTS: The resulting framework includes four domains (health outcomes, health delivery, individual experience, and program implementation) and 19 sub-domains designed to support the development and evaluation of pediatric virtual care programs. We also developed guidance on how to use the framework and illustrate its utility by applying it to an existing pediatric virtual care program. CONCLUSIONS: This virtual care evaluation framework expands on previously developed frameworks by providing additional detail and a structure that supports practical application. It can be used to evaluate a wide range of pediatric virtual care programs in a standardized manner. Use of this comprehensive yet easy to use evaluation framework will inform appropriate implementation and integration of virtual care into routine practice and support its sustainability and continuous improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".