Impact of the COVID-19 Pandemic on Adults With Type 2 Diabetes Care and Clinical Parameters in a Primary Care Setting in Ontario, Canada: A Cross-sectional Study
Bibliographic record
Abstract
OBJECTIVES: Diabetes requires ongoing monitoring and care to prevent long-term adverse health outcomes. In Canada, quarantine restrictions were put into place to address the coronavirus-2019 (COVID-19) pandemic in March 2020. Primary care diabetes clinics limited their in-person services and were advised to manage type 2 diabetes (T2D) through virtual visits and reduce the frequency of routine diabetes-related lab tests and screening. METHODS: This retrospective cross-sectional study used de-identified patient records from a primary care electronic medical records database in Ontario, Canada, to identify people with T2D who had at least 1 health-care touchpoint between March 1, 2018, and February 28, 2021. Outcomes were described on a monthly or yearly basis: 1) number of people with primary care visits (in-person vs virtual); 2) number of people with referrals; 3) number of people with each of the vital/lab measures; and 4) results of the vital/lab measures. RESULTS: A total of 16,845 individuals with T2D were included. Compared with the pre-pandemic period, the COVID-19 period had a 16.8% reduction in the T2D population utilizing any primary care and an increase of 330.4% in the number of people with at least 1 virtual visit. Compared with the pre-pandemic period, fewer people had vital/lab measures in the pandemic period. However, among the people with the test results available, the average values for all tests were similar in the pre- and pandemic periods. CONCLUSION: Further research is needed to understand the impact of the reduction of in-person clinical care on the entire population with T2D.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".