Successes and Challenges of Sustaining Community-Based Programs for People Who Live Alone with Dementia
Bibliographic record
Abstract
Abstract Approximately a quarter of older adults with dementia or cognitive impairment live alone in the US. To address the needs of people living alone with dementia (PLAWD), the Administration for Community Living requires PLAWD-focused services in its Alzheimer’s Disease Program Initiative (ADPI) to address service gaps and improve care delivery for PLAWD. We examined 59 ADPI grants to determine the types of services delivered, and successes and challenges faced in service delivery. Grantees provided care management and/or delivered services such as companionship and support for socialization through buddy programs, phone calls, social and wellness programming, and regular home visits. This study explores the post-grant period experiences of community-based organizations that received federal funding. The study team conducted semi-structured, online interviews with 13 of the 59 grant program directors representing multiple regions of the US. Interviews were audio/video-recorded, transcribed, and manually coded by team members using an inductive approach to identify key themes. Findings highlight the factors that impacted the long-term sustainability of PLAWD-focused services after federal funding ceased. Grantees implemented strategies to maintain and expand services including modifying services, partnering with local service providers and leveraging existing relationships. Findings demonstrate that a variety of services have been successfully sustained for PLAWD. However, common challenges are experienced in sustaining service delivery after initial grant funding ends. PLAWD often lack local care partners and need specialized services and support, including care management, care monitoring, and socialization. Different approaches used and challenges faced in sustaining grant-initiated community programs for PLAWD are discussed.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".