Impact of COVID-19 on Primary Care: Addressing Health Concerns and Patient Experience of Virtual Care
Bibliographic record
Abstract
Introduction: During the height of the pandemic, primary care clinics were shuttered or only seeing urgent cases.Virtual consultations were adopted to ensure patients had their health concerns met.This study sought to explore the primary care experiences of older adult patients during the COVID-19 pandemic, specifically the impact of COVID-19 on the ability of older patients' ability to have their non-COVID-19 health needs addressed, and older patients' specific experiences with virtual care.Methods: Qualitative interviews were conducted over Zoom or telephone and followed an investigator-designed semi-structured interview guide.Interviews were recorded and transcribed verbatim.Thematic analysis was used to make sense of and interpret the data.Findings: Twenty-nine participants (average age 68 years) participated in the study.Participants indicated that they were able to have their health needs addressed despite COVID-19 impacted how primary care was delivered.Impacts included physicians being more rushed, not taking time with new medical concerns in some cases and creating a sense of fear and doom with the strict protocols in place to mitigate the spread of COVID-19.Virtual care was generally well-received by participants, with some exceptions.Advanced age and difficulties with hearing were two of the main reasons for poor experiences with virtual care.Conclusions: Overall, patients in this Study were able to have their health needs addressed.Tailoring virtual care to either phone or videoconferencing for those who have cognitive or sensory 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".