A comprehensive evaluation tool to assess community capacity and readiness for virtual care implementation
Bibliographic record
Abstract
IntroductionThe rapid evolution and implementation of virtual care technologies for clinical use often exceeds the development of standardized implementation protocols, leading to gaps in the equitable and sustainable adoption of virtual care services, particularly in rural and remote areas. This paper introduces a comprehensive evaluation tool designed to assess community capacity and readiness for virtual care.MethodsThe development of the tool was informed by experiences from the Virtual Care and Robotics Program at the University of Saskatchewan. It involved a collaborative, multi-stakeholder approach that engaged healthcare leaders, IT experts and community healthcare workers. This iterative process included defining evaluation categories, mapping evaluative domains and refining the tool into a user-friendly checklist manual.ResultsThe tool identifies three key domains for assessing readiness: clinical needs, technology infrastructure and human resources/workflows. It was piloted in the remote community of Fond du Lac, Saskatchewan, where it successfully qualified the community for implementing telerobotic ultrasound services. The tool facilitated local engagement and highlighted the community's specific needs and readiness, enhancing the implementation process.ConclusionThis evaluation tool contributes to bridging the gap between the rapid deployment of virtual care technologies and the need for comprehensive, standardized implementation protocols. It offers a structured, practical approach to assessing and enhancing community readiness for virtual care while promoting successful clinical implementation and equitable access to healthcare.
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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 | 0.060 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".