Predictors of Mental Health Crises Among Individuals With Intellectual and Developmental Disabilities Enrolled in the START Program
Bibliographic record
Abstract
Objective: Individuals with intellectual and developmental disabilities disproportionately use emergency psychiatric services compared with their neurotypical peers, suggesting that such individuals and their supports are at increased risk for crisis events. This prospective study examined the timing, outcomes, and predictors of mental health crises for this population. Methods: The data came from Systemic, Therapeutic, Assessment, Resources, and Treatment (START), a national model that provides mental health crisis services for those with intellectual and developmental disabilities in the United States. The study included 1,188 individuals from four U.S. regions enrolled between 2018 and 2019. The outcome was urgent crisis contacts with the START program. Baseline and clinical predictors were examined with multivariate regression analyses. Results: More than a quarter had at least one crisis contact, and 9% had three or more. Contacts increased within the initial 3 months of START enrollment, followed by a steep drop-off thereafter; few contacts happened after 1 year. Almost 45% of the contacts occurred after hours, and 30% involved police. Clinical factors predicted crisis contact most robustly, followed by lack of occupational supports. After START crisis intervention, 73% of individuals remained in their primary setting. Conclusions: For individuals with intellectual and developmental disabilities and mental health needs, crisis stabilization resources are needed, including after hours. Results clearly identify times and risk factors for mental health crisis contacts, including frequent involvement with emergency responders. Importantly, gainful employment conveyed benefits for community stabilization. Findings may be leveraged to develop effective mental health crisis intervention services and supports for this underserved group.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".