The association between care modality and hospitalizations and emergency department visits for ambulatory care-sensitive conditions during and after the pandemic in Ontario, Canada
Bibliographic record
Abstract
The COVID-19 pandemic required a rapid transition to virtual care as a key strategy to maintain healthcare access while minimizing virus transmission risks. However, the impact of this shift on hospitalizations and emergency department (ED) visits for ambulatory care-sensitive conditions (ACSCs) remains unclear. This study aims to assess the relationship between the modality of outpatient care for ACSCs and their outcomes in Ontario, Canada. In this population-based retrospective cohort study, we analyzed hospitalization and ED visit data for ACSCs, including diabetes, epilepsy, congestive heart failure, hypertension, and angina, during the pandemic (April 2020 to April 2023) and post-pandemic (May 2023 to August 2023) periods. Monthly trends in hospitalizations and ED visits were evaluated using Generalized Additive Models and Generalized Additive Mixed Models, accounting for the effects of virtual and in-person care within 30 days and 60 days preceding each event. Despite a notable decrease in virtual visits and a corresponding rise in in-person visits, overall hospitalizations and ED visits for ACSCs remained relatively stable. Our analysis found no significant association between care modality and changes in hospitalizations and ED visits, suggesting that virtual care, particularly during the early pandemic, effectively supported chronic disease management and contributed to the stability of acute care needs. In conclusion, virtual care proved to be a sustainable component of ACSC management during and after the COVID-19 pandemic, complementing in-person care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".