Evaluating Virtual Care for Adults with Type 2 Diabetes During the COVID-19 Pandemic: Results From a Pre–Post Retrospective Cohort Pilot Study in a Canadian Family Health Team
Bibliographic record
Abstract
OBJECTIVES: In this study we aimed to determine the feasibility of using electronic medical record (EMR) data to assess whether virtual care provided by a family health team (FHT) located in Ontario during the early COVID-19 pandemic impacted key diabetes indicators, and to identify whether content of diabetes-specific appointments was comparable to in-person care. METHODS: A retrospective pre-post cohort study was performed using patient-level EMR data from eligible FHT patients with type 2 diabetes. Feasibility outcomes were defined with prespecified success criteria. Scientific outcomes included 3 diabetes indicators: glycated hemoglobin (A1C), low-density lipoprotein (LDL), and blood pressure (BP). Indicator results from the prepandemic period (September 2019) and during-pandemic period (March 2020 to March 2021) were recorded and compared. Patient characteristics and content of diabetes-specific appointments were also collected. RESULTS: All feasibility outcomes met predefined criteria for success. No significant differences in A1C, LDL, or BP were identified between pre- and during-pandemic cohorts. Depression screening was performed significantly more frequently during the pandemic (73.85% vs 47.06%, p<0.001) at diabetes-specific appointments compared with the pre-pandemic period. No significant differences in frequency of screening for other complications (i.e. hypoglycemia, foot, eye) were found. CONCLUSIONS: Results signal that virtual care delivery in the during-pandemic period was not associated with significant changes in diabetes indicators or reduced screening for complications; however, many patients did not have BP measurements recorded during the pandemic. Findings support evidence that virtual care may help maintain continuity of care for diabetes management when in-person appointments are not possible.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".