Improving Telehealth Equity in Response to COVID-19 in California: ACTIVATE and Lighthouse
Bibliographic record
Abstract
Background In response to the significant stressors on health care delivery created by COVID-19, CITRIS Health and partner organizations developed 2 telehealth solutions through a rapid co-design process: Lighthouse and ACTIVATE. These programs were developed to support providers serving underserved and vulnerable populations who lack the tools and resources to support patients with chronic illness or who are isolated. These challenges were exacerbated by the COVID-19 pandemic, which increased the need for resources to support vulnerable patients who could not come into a clinic in person or were isolated and lacked access to services. ACTIVATE and Lighthouse apply 2 different telehealth strategies to reach vulnerable populations. Objective This paper presents lessons learned from the design, development, and implementation of 2 innovative telehealth programs developed to improve health care delivery, access to care, digital literacy, and patient outcomes: ACTIVATE and Lighthouse. Methods ACTIVATE is a comprehensive digital health pathway for community health centers that care for those who are medically underserved. ACTIVATE is an innovative, evidence-based, and sustainable telehealth program, designed to benefit vulnerable populations in rural and agricultural communities in the California Central Valley who experience significant health disparities. Lighthouse focuses on connecting older adults in congregate affordable housing, which are settings where residents are particularly vulnerable to isolation, the lack of health care resources, and limited to no access to the internet. Lighthouse provides digital literacy training as well as access to broadband internet with the goal of increasing communication, engagement, and access to health care. Results This presentation will discuss successful design and implementation strategies as well as organizational and policy barriers to program operations. Conclusions In addition to reviewing program and implementation outcomes, strategies for replication and sustainability will be discussed. Although developed in response to COVID-19, the ultimate success of Lighthouse and ACTIVATE is dependent upon its successful scaling beyond the pandemic. Conflicts of Interest None declared.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".