A Summary of Organizations Providing Evidence-Based Dementia Caregiving Programs in Best Programs for Caregiving
Bibliographic record
Abstract
Abstract Best Programs for Caregiving (BPC) is a free online resource of 45 evidence-based dementia caregiving programs. The Public Version of BPC, launched in 2024, enables family/friend caregivers to use a zip code search to find BPC programs available in their communities. Data were analyzed from a structured survey of 206 organizations identified by program developers as delivering their BPC program and used to populate the Public Version. The most common types of delivery organizations were healthcare or single-service community organizations (37; 18.0%), AAAs (37; 18.0%), multi-service community organizations (31; 15.0%), and senior centers/meal programs (28; 13.6%). There were 28 (13.6%) organizations offering a BPC program nationwide to caregivers living anywhere in the US, and 178 offering a program locally or statewide (86.4%). Most organizations were delivering a BPC program partially or fully remote, with only 34 (16.5%) exclusively in-person. An overwhelming majority were delivered exclusively by professionals (160; 77.7%) and were free (175; 85%). Nearly a quarter of programs (49; 23.8%) were adapted for caregivers identifying with diverse communities, most commonly Hispanic and Latino/Latina, Black/African American and LGBTQ. There were 73 (35.8%) programs delivered in languages other than English, with the most frequent offering being Spanish (69). Findings demonstrate that caregivers throughout the US, including those who identify with underserved populations or live in underserved areas, have evidence-based support programs available in their communities, with many being free. Findings also illustrate variation in the characteristics of programs, enabling caregivers to choose options that most closely match their preferences.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".