Virtual care in Ontario community health centres: a cross-sectional study to understand changes in care delivery
Bibliographic record
Abstract
BACKGROUND: There has been a large-scale adoption of virtual delivery of primary care as a result of the COVID-19 pandemic. AIM: In this descriptive study, an equity lens is used to explore the impact of transitioning to greater use of virtual care in community health centres (CHCs) across Ontario, Canada. DESIGN & SETTING: A cross-sectional survey was administered and electronic medical record (EMR) data were extracted from 36 CHCs. METHOD: The survey captured CHCs' experiences with the increased adoption of virtual care. A longitudinal analysis of the EMR data was conducted to evaluate changes in health service delivery. EMR data were extracted monthly for a period of time before the pandemic (April 2019-February 2020) and during (April 2020-February 2021). RESULTS: In comparison with the pre-pandemic period, CHCs experienced a moderate decline in visits made (11%), patients seen (9%), issues addressed (9%), and services provided (15%). During the pandemic period, an average of 54% of visits were conducted virtually, with telephone as the leading virtual modality (96%). Drops in service types ranged from 28%-82%. The distribution of virtual modalities varied according to the provider type. Access to in-person and virtual care did not vary across patient characteristics. CONCLUSION: The results demonstrate a large shift towards virtual delivery while maintaining in-person care. No meaningful differences were found in virtual versus in-person care related to patient characteristics or rurality of centres. Future studies are needed to explore how to best select the appropriate modality for patients and service types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".