Changes in Primary Care Health Services During the COVID-19 Pandemic: A Longitudinal Analysis of Data From Ontario
Bibliographic record
Abstract
The COVID-19 pandemic significantly impacted primary care, but its effect on quality of care is not well understood. We used health administrative data to understand the changes in quality-of-care measures for primary care between October 2018 and April 2022. We examined the following domains: cancer screening, chronic disease (diabetes) management, high-risk prescribing, continuity of care and capacity of primary care services. Colorectal and breast cancer screenings declined after the pandemic and had not returned to baseline by study end. In patients living with diabetes, in-person visits and up-to-date retinopathy screening rates declined after the pandemic declaration and did not return to baseline by study end, while statin prescribing remained stable. High-risk opioid prescribing decreased over time and was not affected by the pandemic. Physician continuity remained stable, though new patient enrollments decreased over the pandemic but returned to baseline by study end. Existing disparities in colorectal cancer screening by income and recent registration widened during the pandemic. In summary, COVID-19 had a variable impact on primary care, with the strongest influence on preventive and chronic disease care that was dependent on in-person visits.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".