Pop-up micro clinics for pre-exposure prophylaxis of immunocompromised patients
Bibliographic record
Abstract
Purpose Health care systems aspire to adopt integration strategies shifting the focus from acute care to a broader focus on community-based health and social services. Real-world examples demonstrating effective delivery of integrated care are essential. Design/methodology/approach In this article, we introduce UHN Connected Care Hub, an innovative model of care comprising an interdisciplinary team designing sustainable, shareable practices across the continuum of care alongside community and health organization partnerships. Findings We describe UHN Connected Care Hub’s ability to identify patients from high-risk population and collaborate to delivery timely care, in detailing the real world experience of this model of care in the organization of a centralized system of micro-clinics to administer a therapeutic for pre-exposure prophylaxis against COVID-19 (Tixagevimab/cilgavimab [Evusheld]) in a population of immunocompromised patients. Practical implications Having a centralized system of micro-clinics for care delivery presents opportunities for increased adaptability, patient accessibility, enhanced community partnerships and integratedness. Expansion in the scope of services could also create new opportunities in preventative therapies for optimizing the cost effectiveness and quality of health care provided at the population level. Originality/value There is limited evidence on how to efficiently deliver integrated care, particularly to vulnerable and co-morbid patients. We discuss how dynamic organizations with proper infrastructure and a network of healthcare partnerships may allow a more fluid response to rapidly changing policies and procedures and facilitate preparedness for future health care crises or pandemics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.019 | 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".