The Power of Population Health Data on Aging and Intellectual and Developmental Disabilities: Reactions of Knowledge Users
Bibliographic record
Abstract
Abstract Recent work in Ontario (Canada) revealed that adults with intellectual and developmental disabilities experience higher rates of frailty and use of aging care services at earlier ages than the general population, and that the subset aged 65+ years is increasing. This paper describes the reaction of knowledge users to study findings and implications for policy and practice. A knowledge transfer webinar was held with nearly 200 people representing different regions of the province, participant types (family members, service providers, decision makers, researchers), and sectors (health and developmental services). Most participants viewed health and developmental services systems as not ready for the aging population with intellectual and developmental disabilities for two main reasons: insufficient cross‐sector expertise and inadequate funding. The need for healthcare, challenged informal supports, lack of services, and the desire for independence were thought to drive higher use of home care among younger adults, while inadequacies within the developmental services sector, challenged informal supports, medical and care needs, lack of community supports, and the need for coordinated cross‐sector services were noted as contributing to admissions to long‐term care. There is a lack of evidence‐based information on aging and intellectual and developmental disabilities. Ongoing access to quality, population‐level data on the number and needs of persons with intellectual and developmental disabilities is needed to improve policies and practices to support aging in the community. Persons working in health and developmental services had a shared understanding of the need for system reform, better collaboration, and integration of resources. Both sectors also viewed admission to long‐term care as particularly problematic. The province‐wide webinar brought together persons with various levels of responsibility from different sectors. Future exchanges should focus on identifying and promoting best practices.
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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 | 0.004 | 0.397 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".