Effects of the COVID-19 pandemic on primary care for diabetes in Canada: Results from a mixed-methods study
Bibliographic record
Abstract
Context: In Canada, most diabetes care is provided within primary care. Primary care experienced challenges during the COVID-19 pandemic, such as reduced access to care. Objective: To understand if the pandemic resulted in changes in care for patients with diabetes. Study design & Analysis: Parallel explanatory mixed methods study including: 1) A retrospective pre-post quantitative study was conducted to compare diabetes indicators pre-pandemic (Jun/22/2018 - Mar/12/2020) and during the pandemic (Mar/13/2020 – Dec/3/2021), using data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database. 2) Qualitative interviews were conducted to understand patient experiences with navigating diabetes care during the pandemic and analyzed using qualitative description. Setting & Dataset: The CPCSSN database contains de-identified patient-level electronic medical record data from 13 primary care research networks across Canada. Population Studied: Using CPCSSN data, we defined a cohort of patients aged 50-105 with diabetes diagnosed before the pre-pandemic period. Qualitative interviews were conducted with Ontario patients aged > 50 with diagnosis of (or receiving treatment for) type 2 diabetes prior to pandemic onset. Outcomes: Diabetes indicators included frequency and results of HbA1c tests and blood pressure (BP) measurements. Interviews elicited patient experiences with changes to their diabetes care during the pandemic. Results: We identified 84,617 patients for the cohort study. Number of people with >1 HbA1c test decreased by 10% during the pandemic. Median (IQR) HbA1c tests decreased from 3(1,5) pre-pandemic to 2(1,4) during-pandemic. However, mean HbA1c scores did not change significantly over time. Number of people with >1 BP measurement decreased by 23%. Median (IQR) BP measurements decreased from 3(0,6) pre-pandemic to 1(0,2) during-pandemic. Yet, mean BP did not meaningfully change over time. Qualitative interviews (n =19) identified disruptions to ongoing diabetes care: access to supplies and medications, limited encounters with physicians or specialists (e.g., virtual care), and navigating at-home monitoring and care (e.g., blood sugar, BP, exercise, lifestyle). Conclusions: Both quantitative and qualitative inquiries showed that despite decreases in the frequency of monitoring tests and decreased oversight from physicians and specialists due to public health restrictions, diabetes care indicators remained relatively unchanged.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 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".