Primary Care Provider Experiences and Perspectives of Virtual Primary Care Visits
Bibliographic record
Abstract
Context: The onset of COVID-19 and rapid response to public health restrictions prompted increased use of virtual care (VC). Prior to the pandemic there was low utilization of technology for communication and many primary care providers (PCPs) had little to no experience with VC. Thus, greater use of VC required adjustments to how health care was provided. Literature specific to VC has focused on communication modalities with lingering questions regarding how providers have been impacted. Objective: To explore virtual care (VC) adoption and use in Manitoba, Canada from the perspective of health care providers. Study Design and Analysis: Qualitative phenomenological approach using content analysis performed by two members of the team including a patient partner. Setting: Six focus group sessions conducted virtually. Population Studied: 21 primary care providers in Manitoba, Canada. Intervention/instrument: Exploration of experiences including benefits and challenges of VC, the impacts on provider workload, quality of care and clinic workflow, as well as recommendations for sustainable VC. Results: Options for VC visits were limited due to logistical and accessibility challenges faced by providers and patients. Telephone visits were most common. In some instances, VC was useful for screening and assessment; however, the lack of visual cues challenged the delivery of care. Respondents felt consults required in-depth history-taking and focused exploratory questioning, but also raised the concern of having to balance risk and ruling out more serious conditions. One provider referred to VC as “a great addition to the whole care package,” generally offering convenience and greater accessibility for some but limitations for others. Providers experienced more flexibility with their practice, which benefitted their well-being and evolved as providers developed individual strategies for the ‘right mix’ of virtual and in-person care. Conclusion: The perspectives gained from one of the key ‘user’ groups within the health care system will likely resonate with health care providers across Canada and beyond, who also were faced with implementing VC in a rapidly changing environment during the pandemic. Through the experiences of health care providers, we gain a better understanding of VC within clinical practice; where challenges need to be mitigated; and the recommendations for sustained quality VC beyond the pandemic era.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".