Virtual care: a design research discovery and strategic futures model for the Canadian healthcare system
Bibliographic record
Abstract
The COVID 19 pandemic necessitated a rapid implementation of virtual care within the Canadian healthcare system generating previously unimagined levels of virtual care uptake and accessibility. The transition to virtual care provided benefits for both patients and providers including a reduction in cost, time saved, and greater protection from infection. However, to date, the system in the Canadian province of Ontario has focussed on ‘replacing’ discrete in-person ‘moments’ of care with digital interactions such as phone and video visits. This design research study contributes to the health design community by incorporating a strategic futures approach to existing discussions surrounding virtual care. Collecting, analyzing and adding patient and primary care provider voices through this design research study provides new insights into virtual care experiential gaps and highlights opportunities for virtual care within primary care modalities. As a result of this new data, and through consultation with stakeholders, a roadmap for future virtual care possibilities in Ontario was developed answering noted needs of patients and providers by extending digital health interactions across a broader spectrum of synchronous and asynchronous care modalities and folding in an amalgam of digital, virtual, and in person connection for patient care experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".