Pediatric primary care in Ontario and Manitoba after the onset of the COVID-19 pandemic: a population-based study
Bibliographic record
Abstract
BACKGROUND: There were large disruptions to health care services after the onset of the COVID-19 pandemic. We sought to describe the extent to which pandemic-related changes in service delivery and access affected use of primary care for children overall and by equity strata in the 9 months after pandemic onset in Manitoba and Ontario. METHODS: We performed a population-based study of children aged 17 years or less with provincial health insurance in Ontario or Manitoba before and during the COVID-19 pandemic (Jan. 1, 2017-Nov. 28, 2020). We calculated the weekly rates of in-person and virtual primary care well-child and sick visits, overall and by age group, neighbourhood material deprivation level, rurality and immigrant status, and assessed changes in visit rates after COVID-19 restrictions were imposed compared to expected baseline rates calculated for the 3 years before pandemic onset. RESULTS: Among almost 3 million children in Ontario and more than 300 000 children in Manitoba, primary care visit rates declined to 0.80 (95% confidence interval [CI] 0.77-0.82) of expected in Ontario and 0.82 (95% CI 0.79-0.84) of expected in Manitoba in the 9 months after the onset of the pandemic. Virtual visits accounted for 53% and 29% of visits in Ontario and Manitoba, respectively. The largest monthly decreases in visits occurred in April 2020. Although visit rates increased slowly after April 2020, they had not returned to prerestriction levels by November 2020 in either province. Children aged more than 1 year to 12 years experienced the greatest decrease in visits, especially for well-child care. Compared to prepandemic levels, visit rates were lowest among rural Manitobans, urban Ontarians and Ontarians in low-income neighbourhoods. INTERPRETATION: During the study period, the pandemic contributed to rapid, immediate and inequitable decreases in primary care use, with some recovery and a substantial shift to virtual care. Postpandemic planning must consider the need for catch-up visits, and the long-term impacts warrant further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| 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) | 0.001 | 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".