Healthcare for Persons with Intellectual and Developmental Disability in the Community
Bibliographic record
Abstract
INTRODUCTION: While there has been impressive progress in creating and improving community healthcare delivery systems that support people with intellectual and developmental disabilities (IDD), there is much more that can and should be done. METHODS: This paper offers a review of healthcare delivery concepts on which new models are being developed, while also establishing an historical context. We review the need for creating fully integrated models of healthcare, and at the same time offer practical considerations that range from specific healthcare delivery system components to the need to expand our approach to training healthcare providers. The models and delivery systems, and the areas of needed focus in their development are reviewed to set a starting point for more and greater work going forward. CONCLUSION: Today, we celebrate longer life spans of people with IDD, increased attention to the benefits of healthcare that is responsive to their needs, and the development of important healthcare delivery systems that are customized to their needs. We also know that the growing body of research on health status offers incentive to continue developing healthcare structures for people with IDD by training healthcare providers about the needs of people with IDD, by establishing systems of care that integrate acute healthcare with long-term services and support, by developing IDD medicine as a specialty, and by building health promotion and wellness resources to provide people with IDD a set of preventative health supports.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".