Describing primary care patterns before and during the COVID-19 pandemic across Canada: a quasi-experimental pre–post design cohort study using national practice-based research network data
Bibliographic record
Abstract
OBJECTIVE: The objective was to analyse how the pandemic affected primary care access and comprehensiveness in chronic disease management by comparing primary care patterns before and during the early COVID-19 pandemic. DESIGN: We conducted a quasi-experimental pre-post design cohort study and reported indicators for the 21 months before and after the onset of the COVID-19 pandemic. SETTING: We used electronic medical record data from primary care clinics enrolled in the Canadian Primary Care Sentinel Surveillance Network from 1 January 2018 to 31 December 2021. POPULATION: The study population included patients (n=919 928) aged 18 years or older with at least one primary care contact from 12 March 2018 to 12 March 2020, in Canada. OUTCOME MEASURES: The study indicators included three indicators measuring access to primary care (encounters, blood pressure measurements and lab tests) and three for comprehensiveness (diagnoses, non-COVID-19 vaccines administered and referrals). RESULTS: 67.3% of the cohort was aged ≥40 years, 56.4% were female and 53.5% were from Ontario, Canada. Fewer patients received an encounter during the pandemic (91.5% to 81.5%), while the median (IQR) number of encounters remained the same (5 (2-1)) for those with access. Fewer patients received a blood pressure measurement (47.9% to 31.8%), and patients received fewer measurements during the pandemic (2 (1-4) to 1 (0-2)). CONCLUSIONS: Encounters with primary care remained consistent during the pandemic, but in-person care, such as lab tests and blood pressure measurements, decreased. In-person care indicators followed temporally to national COVID-19 case counts during the pandemic.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".