Bibliographic record
Abstract
<div> Background The COVID-19 pandemic has reshaped healthcare delivery worldwide. Objective To explore potential changes in the reasons for visits and modality of care in primary care settings through the <u>Int</u>ernational Conso<u>r</u>tium of <u>P</u>rimary Care B<u>i</u>g <u>D</u>ata Researchers (INTRePID). Methods We conducted a cross-sectional, retrospective study from 2018–2021. We examined visit volume, modality, and reasons for visits to primary care in Argentina, Australia, Canada, China, Peru, Norway, Singapore, Sweden, and the USA. The analysis involved a comparison between the pre-pandemic and pandemic periods. Results There were more than 215 million visits from over 38 million patients during the study period in INTRePID primary care settings. Most INTRePID countries experienced a decline in monthly visit rates during the first year of the pandemic, with rate ratios (RR) and 95% confidence intervals (CI) ranging from RR:0.57 (95%CI:0.49–0.66) to RR:0.90 (95%CI:0.83–0.98), except for in Canada (RR:0.99, 95%CI:0.94–1.05) and Norway (RR:1.00, 95%CI:0.92–1.10), where rates remained stable and in Australia where rates increased (RR:1.19, 95%CI:1.11–1.28). Argentina, China, and Singapore had limited or no adoption of virtual care, whereas the remaining INTRePID countries varied in the extent of virtual care utilization. In Peru, virtual visits accounted for 7.34% (95%CI:7.33%-7.35%) of all interactions in the initial year of the pandemic, dipping to 5.22% (95%CI:5.21%-5.23%) in the subsequent year. However, in Canada 75.30% (95%CI:75.20%-75.40%) of the visits in the first year were virtual, decreasing to 62.77% (95%CI:62.66%-62.88%) in the second year. Diabetes, hypertension and/or hyperlipidemia and general health exams were in the top 10 reasons for visits in 2019 for all countries. Anxiety, depression and/or other mental health related reasons were among the top 10 reasons for virtual visits in all countries that had virtual care. Conclusions The pandemic resulted in changes in reasons for visits to primary care, with virtual care mitigating visit volume disruptions in many countries. </div>
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.215 | 0.013 |
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; both teacher heads agree on what is shown here.
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".