Aging Well Together: Promoting Brain Health for Aging Individuals with IDD and Their Families
Bibliographic record
Abstract
Adults with intellectual and developmental disabilities (IDD) have been historically excluded from participating in aging-focused research, continue to experience barriers accessing appropriate health care, and encounter distinct challenges as they age compared to people without IDD. These challenges and their effects often extend to their immediate support system, including family caregivers, including parents, siblings, and other kin. Family caregivers, often in the role of 'perpetual caregivers,' can struggle to support aging adults with IDD as they also concurrently experience the effects of their own aging, potentially hindering their ability to provide lifelong care. As parents age, the burden of care may shift to others within this support network such as siblings who, due to various constraints (e.g., geographic location, personal obligations, and financial restrictions) can also experience challenging caregiving responsibilities. Research on the mental health of family caregivers has predominantly focused either on the impact of caregiving among parents of people with IDD (and to a lesser extent on siblings) or on late-life caregivers of aging adults without IDD. This leaves a significant gap in understanding how to support caregivers during mid-late adulthood as their family member with IDD ages or as they themselves undergo age-related changes. In this paper, we provide a brief overview of research on family caregiving of older adults with IDD, followed by a description of a national effort, The Brain Health-IDD initiative. This initiative aims to develop strategies that will support the promotion of brain health in aging adults with IDD and their family caregivers. As part of this effort, family caregivers, both siblings and parents of adults with IDD are working together with researchers and clinicians to co-design, co-deliver and evaluate a virtual education program for families. The paper concludes by reflecting on the program's importance from the perspective of sibling and parent caregivers. This inclusive approach emphasizes the broader responsibility of supporting families navigating the challenges of disability and aging in mid-late adulthood, highlighting the need for support and awareness across all levels of care.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".