A Systems-Level Evaluation Framework for Virtual Care
Bibliographic record
Abstract
The virtual care landscape is significantly changing, largely due to an increased demand initiated by the COVID-19 pandemic and the evolution of technology. Complex questions about how to best leverage virtual care and its impact remain unanswered. Our team developed a systems-level evaluation framework to inform virtual care service design and evaluation to take a more comprehensive approach to planning and implementing virtual care. We designed the framework for application in Alberta Health Services (AHS) by engaging virtual care users (patients, families and healthcare providers), implementation staff and decision makers across the organization. Here we report our design process and key lessons learned. The framework received endorsement by AHS senior leadership for application across the system. Our next step is to test the framework. By sharing our design process and experiences, we aim to help inform other national and international jurisdictions plan virtual care evaluations within their context.
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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 | 0.253 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".