Promoting Brain Health and Resilience in Family Caregivers of Adults with Intellectual and/or Developmental Disabilities
Bibliographic record
Abstract
Adults with intellectual and/or developmental disabilities (IDD) face increased risks of age-related health issues, including dementia, yet current brain health initiatives often overlook the needs of this unique population. To address this gap, our team developed the Brain Health-IDD program - a co-designed virtual education initiative aimed at promoting brain health among aging adults with IDD (aged 40+), and family caregivers. The program utilizes an integrated Knowledge Mobilization (KMb) approach, engaging individuals with lived experience, community members, and professionals throughout the co-design process to ensure relevance and inclusivity. Family caregivers of individuals with IDD are at a heightened risk of developing dementia themselves, due to the chronic stress associated with the caregiver role. Although some supports focus on assisting aging parents and siblings supporting these individuals, minimal attention has focused on the long term impacts of caregiving on their brain health. The Brain Health-IDD Family course aims to mitigate this risk by providing caregivers with critical information on brain health, stress management, and strategies for improving their own cognitive and physical well-being. By improving caregiver self-efficacy, the program seeks to reduce stress, promote resilience, and ultimately improve the long-term health outcomes for caregivers, in addition to the individuals they support. Because the program is virtual, it is available to aging family caregivers from across Canada, and not reliant on local expertise. The Brain Health-IDD Family course consists of 6 weekly 90-minute virtual sessions, co-led by interdisciplinary teams of caregivers, brain health experts and clinician scientists. The primary objectives of the program are to encourage changes in health behaviors, physical and mental well-being, and the initiation of cognitive screening among participants. To evaluate impact of our Program, we use a mixed-methods, pre-post study to collect caregivers' outcomes at baseline, 7 weeks (post-intervention), and 18 weeks. Data from satisfaction surveys, qualitative interviews, and open-text surveys from the first cohort have informed the iterative refinement of course content and delivery. The third cohort will participate in the program between February and March of 2025, with analysis completed on over 100 aging family caregivers by summer of 2025.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".