Bibliographic record
Abstract
<div> Digital health technologies used in primary care, referred to as, virtual primary care, allow patients to interact with primary healthcare professionals remotely though the current iteration of virtual primary care may also come with several unintended consequences, such as accessibility barriers and cream skimming. The World Health Organization (WHO) has a well-established framework to understand the functional components of health systems. However, the existing building blocks framework does not sufficiently account for the disruptive and multi-modal impact of digital transformations. In this review, we aimed to develop the first iteration of this updated framework by reviewing the deployment of virtual primary care systems in five leading countries: Canada, Finland, Germany and Sweden and the United Kingdom (England). We found that all five countries have taken different approaches with the deployment of virtual primary care, yet seven common themes were highlighted across countries: (1) stated policy objectives, (2) regulation and governance, (3) financing and reimbursement, (4) delivery and integration, (5) workforce training and support, (6) IT systems and data sharing, and (7) the extent of patient involvement in the virtual primary care system. The conceptual framework that was derived from these findings offers a set of guiding principles that can facilitate the assessment of virtual primary care in health system settings. </div>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.056 | 0.004 |
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; both teacher heads agree on what is shown here.
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".