SWARM: A regional health system’s intervention approach to COVID-19 outbreaks in nursing and adult care homes in rural eastern North Carolina
Bibliographic record
Abstract
Background: Incessant, COVID-19 outbreaks occurring in nursing and adult care homes are a serious public health concern that continues to create significant healthcare crisis management challenges. Adult care facilities often lack in-house capacity and capability to safely treat its ill residents, while hospitals are strained to balance the influx of patients, allocate scarce resources and protect healthcare workers.Objectives: This project sought to implement a regional, community engaged, intervention model to assist nursing and adult care homes in reducing or preventing outbreaks and risks associated with COVID-19 in rural eastern North Carolina (N.C.).Methods: Design/Setting: Through collaborations between Vidant Health (VH), health departments and a network of community partners, a shared intervention plan was created and implemented to monitor nursing and adult care homes for COVID-19-related outbreaks across 29 counties in rural eastern N.C. A “Strike” team or “Swarm (SWARM) approach was developed as an operationalized concept for rapidly responding to nursing and adult care home outbreaks while providing an array of services and interventions to help prevent the spread of COVID-19. Comparative analysis was conducted between the mean number of COVID-19-related cases, deaths and length of outbreak time in VH service contracted, SWARM facilities (n = 12) and all other non-service contracted, or non-SWARM facilities (n = 155) in N.C.Results: Nursing and adult care homes under service contract using our SWARM approach experienced fewer average number of COVID-19-related resident ill cases (24.4 vs 29.0), and deaths (1.2 vs. 3.9). The length of outbreak recovery time was far less among SWARM facilities than non-participating, non-SWARM facilities (17.1 vs. 25.4; p < .034).Conclusions: By actively monitoring key indicators, engaging in daily communication with local partners and providing rapid response, VH’s SWARM approach provides a proactive method for preventing further spread of COVID-19 in adult care facilities and communities.
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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) | 0.000 | 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 teacher head, 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".