Frailty and Intellectual and Developmental Disabilities: a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Individuals with intellectual and developmental disabilities (IDD) are both living longer than in previous generations and experiencing premature aging. Improved understanding of frailty in this aging population may inform community supports and avoid negative outcomes. METHODS: The objective of this study was to review the literature on frailty and IDD and determine areas for future research and application. The methodological framework for a scoping review as developed by H. Arksey and L. O'Malley was applied to identify and select original studies published since 2000. RESULTS: Seventeen studies were identified; these were based on the work of researchers from four research programs. The studies utilized six measures of frailty, including two frailty indices, the VFQ-ID(-R), the frailty phenotype, and the frailty marker. Frailty was equally studied as an outcome and as predictor for other outcomes (e.g., mobility, falls, care intensity, institutionalization, and survival). CONCLUSIONS: There is evidence of a growing interest in the measurement of frailty in aging adults with IDD. As in the general population, frailty in this group is associated with many negative outcomes. While a few measures have emerged, more work is required to replicate results, validate tools, and test the feasibility of applying frailty measures in practice and to inform policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".