COVID-19 Assessment and Testing in Rural Communities During the Pandemic: Cross-sectional Analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND The COVID-19 pandemic exacerbated the need for urgent improvements in access to health care for rural, remote, and underserviced communities. The Renfrew County Virtual Triage and Assessment Centre (VTAC) was designed to provide access to COVID-19 testing and assessment with a family physician. The goal was to protect emergency departments and 911 paramedics while ensuring that nobody was left at home, suffering in silence. Residents were encouraged to call their own family physician for any urgent health needs. If they did not have a family physician or could not access their usual primary care provider, then they could call VTAC. This study reports on the output of a service model offering access to assessment and COVID-19 testing through a blend of virtual and in-person care options by a multidisciplinary team. OBJECTIVE The purpose of this study was to assess the ability of VTAC to provide access to COVID-19 assessment and testing across rural, remote, and underserviced communities. METHODS We conducted a cross-sectional analysis of the data derived from the cases handled by VTAC between March 27, 2020 (launch day), and September 30, 2020. RESULTS Residents from all 19 census subdivisions and municipalities of Renfrew County accessed VTAC. A total of 10,086 family physician assessments were completed (average 64 per day). Of these, 8535 (84.6%) assessments were to unique patient users. Thirty physicians provided care. Using digital equipment setup in the patients’ home, 31 patients were monitored remotely. VTAC community paramedics completed 14,378 COVID-19 tests and 3875 home visits. CONCLUSIONS Renfrew County’s experience suggests that there is tremendous synergy between family physicians and community paramedics in providing access to COVID-19 assessment and COVID-19 testing. The blended model of virtual and in-person care is well suited to provide improved access to other aspects of health care post pandemic, particularly for patients without a family physician.
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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 | 0.003 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".