Impact of COVID-19 on Primary Care: Addressing Health Concerns and Older Patient Experience of Virtual Care
Bibliographic record
Abstract
Context: COVID-19 has disrupted routine care for many patients. During the height of the pandemic, when primary care clinics were shuttered or only seeing urgent cases, patients postponed seeking routine care and few options for community-based care existed. As a workaround, virtual consultations—remote access using any form of communication or technology—were adopted. This was not without limitations, especially for older adults. Objective: This study sought to explore the primary care experiences of older adult patients and whether their health needs were addressed via in-person and virtual consultation during the first four waves of the COVID-19 pandemic. Study Design and Analysis: This study employed a qualitative inquiry using interviews. Population Studied: Particpants included adults in a large western Canadian city who were 50 years of age and older, who had visited a primary care provider since March 2020 for a health concern not related to COVID-19. Interviews took place between August and October 2021. Instrument: Qualitative interviews were conducted over Zoom or telephone (based on patient comfort and access to video technology) and followed an investigator-designed semi-structured interview guide. Interviews were recorded and transcribed verbatim. Outcome Measures: Thematic analysis was used to make sense of and interpret the data. A codebook was developed, and from this, themes were determined based on their relevance to the data and the research purposes. Results: Thirty-eight participants (23 women and 15 men, average age 61 years [range 50-87]) participated. Participants were generally satisfied with the care they received from their primary care physicians. Those who had a long relationship with their practitioner could rely on previously built rapport. Some participants reported preferring virtual care to save on time spent in waiting rooms and the cost of transportation. Concerns presented included physicians being more rushed than usual, not taking time with new medical concerns, and creating a sense of fear and doom with the strict protocols in place to mitigate the spread of COVID-19. Conclusions: Overall, patients were satisfied with the care they received from their primary care physicians. Tailoring virtual care to either phone or videoconferencing for those who have hearing impairments, language barriers, or poor connections (and who many need to see non-verbal cues or read lips) is important.
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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 | 0.005 | 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 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".