Open infrastructure for the caregiver research gap
Family caregivers are 63 million people in the United States. They are tracked inconsistently, measured late or not at all, and largely absent from the longitudinal data that shapes care policy and workforce programs.
GiveCare Labs exists to change that. Half of what we build is open source. The rest runs a live service designed to generate the kind of forward-looking, caregiver-centered data that does not yet exist at scale.
What the caregiver experiences
Chief of staff, not companion
GiveCare is not a chatbot. It tracks caregiver-load signals across pressure zones, connects dots over time, and surfaces benefits pre-screening signals before they think to ask.
Closing the action gap
Caregivers often know they need something but cannot act on it. GiveCare moves from identification to preparation: benefits pre-screening signals, resource suggestions, and caregiver-owned next steps.
Built for equity
SMS is a deliberate choice. Most caregivers do not have access to sophisticated AI tools. The channel is designed to reach the people who need support most.
Future research: professional caregivers
The same pressure zones may be useful for paid caregivers, including home health aides, CNAs, and direct support professionals, but the first product scope is unpaid family caregivers.
What the data layer produces
Unit of measurement
Caregiver, not patient
Collection method
SMS check-ins and assessment snapshots over time
Dimensions tracked
Caregiver pressure zones, rolling GiveCare Score, benefits pre-screening signals
Population view
Anonymized aggregate — no PII at the employer or research level
Longitudinal
Repeated snapshots over time, not a one-time cross-section
Primary scope
Unpaid family caregivers first
Future research
Professional-caregiver applications require separate study
Open projects
Caregiver-Specific Social Determinants Framework
GC-SDOH-30
A caregiver-specific 30-item framework that maps caregiver burden across six zones: social support, physical health and energy, housing and environment, financial strain, system navigation, and emotional wellbeing.
Read the frameworkSafety Evaluation for Caregiving AI
InvisibleBench
The first open benchmark designed to evaluate AI safety across long-term caregiving relationships — testing across 3–20+ turn scenarios, identifying failure modes that only emerge over time.
View resultsInterested in collaborating?
We're looking for research partners, pilot programs, and policy collaborators who want to work with longitudinal caregiver-load data.
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