Sociodemographic Differences in Patient Experience with Primary Care during COVID-19
Bibliographic record
Abstract
Context: The COVID-19 pandemic has changed the way health care is delivered with significant increases in the use of virtual care. This has raised concerns about possible negative effects on patient experience and access for marginalized populations. Objective: The project aimed to understand patient experience in primary care during the COVID-19 pandemic differed by patient sociodemographic characteristics, particularly related to care seeking behaviors and use, comfort, and views of virtual care. Study Design and Analysis: Cross-sectional survey conducted between May and June 2000. We used chi-squared tests to compare responses by patient sociodemographic characteristics. Setting or Dataset: Thirteen family medicine clinics associated with the University of Toronto. Population Studied: All primary care patients with a valid email address on file and a date of birth during the months of March, April or May. Intervention/Instrument: An anonymous web-based survey emailed to patients. Outcome Measures: Access including the proportion of patients who reported seeking urgent care and the timeliness of the urgent appointment. Virtual care experience including the proportion of patients who used various modalities, their comfort levels with it and whether they wanted it to continue post-pandemic. Results: In total, 7482 participants responded to the survey. Most respondents received care during the study period (68%). Those who reported trouble making ends meet and those with lower self-rated health were more likely to report seeking urgent care, but less likely to report receiving a timely appointment. Patients were generally comfortable with phone (92%), video (95%) and email or secure messaging (91%) but those who reported difficulty making ends meet, poor or fair health and arriving in Canada in the last 10 years, were less comfortable with digital modalities and less interested in these options being available into the future as part of their care. Conclusion: Our study suggests that newcomers, those with a lower income, and those reporting lower health have a stronger preference and comfort for in-person primary care. Further research should explore potential barriers to virtual care and equitable access and how these can be addressed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".