Virtual Primary Care Implementation During COVID-19 in High-Income Countries: A Scoping Review
Bibliographic record
Abstract
Background: The purpose of this scoping review was to map the challenges, strategies, and lessons learned from high-income countries that can be mobilized to inform decision-makers on how to best implement virtual primary care services during and after the COVID-19 pandemic. Findings of our scoping review identified the barriers and strategies within the Quadruple Aim components, which may prove to be an effective implementation strategy for virtual care adoption in primary care settings. Materials and Methods: The two concepts of virtual care and COVID-19 were searched in MEDLINE, EMBASE, and CINAHL on August 10, 2020, and Scopus was searched on August 15, 2020. The database searches returned 10,549 citations and an additional 766 citations were retrieved from searching the citations from the reference lists of articles that met all inclusion criteria. A total of 1,260 full-text articles were reviewed of which 38 articles met the eligibility criteria for inclusion in the review. Results: Seven key barriers and strategies were identified for the implementation of virtual primary care. Of the 38 articles included, the key barriers identified were equitable access to care ( n = 20; 53%), lack of funding for virtual care ( n = 14; 37%), negative patient and clinician perception ( n = 11, 29%), lack of regulatory policies ( n = 10, 26%), inadequate clinical workflows ( n = 9, 21), lack of virtual care infrastructure ( n = 8, 21%), and insufficient virtual care training and education ( n = 5, 13%). Strategies included the following: increased funding ( n = 15, 39%), improving clinical workflows ( n = 13, 34%), appropriate education and training ( n = 11, 29%), improving virtual care infrastructure and patient equity ( n = 7, 18%), establishing regulatory policies ( n = 5, 13%), and improving patient and clinician perceptions ( n = 3, 7%). Conclusions: As many countries enter potential subsequent waves of the COVID-19 pandemic, applying early lessons learned to mitigate implementation barriers can help with the transition to equitable and appropriate virtual primary care services.
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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 | 0.033 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".