Meet Caregivers Where They Are: A Remote Intervention Connecting Caregivers to Community Resources
Bibliographic record
Abstract
Abstract Informal caregivers of people with Alzheimer’s disease and related dementias (ADRD) are a vulnerable, often isolated population with high rates of financial strain and need for community resource supports. Little is known about how best to connect these caregivers to resources, especially during the COVID-19 pandemic. CommunityRx-Caregiver is an evidence-based intervention that connects caregivers to community resources for basic needs, wellness, and caregiving. Using preliminary data from a randomized trial of CommunityRx-Caregiver (N=344), we examined caregivers’ baseline confidence in finding community resources and their engagement in the CommunityRx-Caregiver intervention. Caregivers enrolled December 2020-February 2021 (n=26) received (1) personalized lists of community resources via text message (HealtheRx), (2) access to an online resource portal (FindRx) and (3) automated texts offering support for finding resources. Most caregivers were female (65%), Black (92%), >60 years old (64%) and 44% reported very good or excellent health. Nearly half of caregivers (46%) were completely confident in finding community resources. Overall, 81% of caregivers engaged with a text message or the FindRx. Nearly two-thirds (65%) of caregivers responded to at least one text message. More than a quarter (27%) used the FindRx tool; 5/7 of those shared FindRx resources with others. Caregivers sought resources including in-home personal care, exercise classes and support groups. Caregivers of people with ADRD, many of whom had low confidence in finding resources, engaged with a multi-modal information technology-based intervention to obtain community resource support. These preliminary findings suggest caregivers were receptive to a remotely-delivered community referral intervention during the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".