Changes in Reasons for Visits to Primary Care as a Result of the COVID-19 Pandemic: by INTRePID
Bibliographic record
Abstract
Context: The COVID-19 pandemic has resulted in changes in healthcare delivery in many countries around the world. Objective: To examine the impact of the pandemic on reasons for visits to primary care through the International Consortium of Primary Care Big Data Researchers (INTRePID). Study Design and Analysis: Cross-sectional retrospective analysis of visit volume, modality and reason for visit from 2018-2021. Setting: Patients seen in primary care settings in Argentina, Australia, Canada, China, Peru, Norway, Singapore, Sweden and USA. Outcome Measures: Monthly visit volume, rates of virtual vs in-person visits for the top 10 reasons for visits to primary care and for common conditions. Results: There were over 215 million visits to primary care in INTRePID countries during the study period. The average monthly visit volume decreased in the first year of the pandemic for INTRePID countries (-20.4% to -43.5%, p=.03 to <.001) except for in Norway, Canada and Sweden (.3%, -.8% and - 9.7%, p=.68, .84, .11 respectively) and increased in Australia (+19%, p=0.013). While Argentina, China and Singapore had little to no virtual care, in the other INTRePID countries the average monthly virtual visit rate ranged from a low in Peru (7.3% first year, 5.2 % second year of the pandemic) to a high in Canada (75.8% first year, 62.5% second year of the pandemic). For anxiety/depression the average monthly visit volume in the first year of the pandemic was higher than pre-pandemic in Australia, Canada, Peru and Singapore (18.9% to 42.2%, p=.004 to <.001). Average monthly visit volume for coughs and colds dropped for all countries in the first year of the pandemic (-47.0% to -86.5%, p=.92 to <.001). Conclusions: While visits to primary care generally declined, the rapid introduction of virtual visits mitigated much of the visit volume disruption in many countries. The pandemic resulted in changes in how primary care is delivered and some changes in what is seen in primary care. It appears that virtual care is likely to be part of a new normal in primary care delivery in many countries.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".