Virtual Care as Deployed to Improve Access to Primary Care in 3 Canadian Provinces: A policy focused qualitative study
Bibliographic record
Abstract
CONTEXT The COVID-19 pandemic accelerated moves towards the virtual delivery of primary care in Canada. As this occurred debates over how virtual primary care (VPC) ought to be encouraged or discouraged, governed and paid for have intensified. Within these high-level debates there has been little attention paid to the details of which technologies are being deployed to deliver VPC and how those technologies interact with non-technical factors to ensure efficiency and effectiveness. OBJECTIVE As part of drawing out pragmatic considerations for policy makers, identifying the various constellations of VPC technology that are being deployed; characterizing how key informants see them working and to what purpose; and describing how those technologies are interacting with non-technical factors to shape success. DESIGN & ANALYSIS 29 qualitative interviews with mid-level VPC experts from the provinces of Alberta, Nova Scotia, and Ontario. Data were coded and analyzed using an Interpretive Description approach. Results From basic phone calls to advanced multi-platform multi-disciplinary case conferences, participants saw VPC technologies as improving access to primary care by increasing efficiency and coordination. Specifically VPC was deployed to improve the accessibility, availability, and accommodativeness of care. VPC technologies interacted with and relied on: human efforts, funding models, and the institutional contexts in which they were deployed. CONCLUSIONS Policy-makers seeking to optimize VPC will want to consider ways to support not just purchases of technology, but: the human effort required to choose and manage technology; the funding mechanisms that incentivize the efficient use technology; and the institutional contexts and cultures that underpin access improvements through technology.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".