Supporting older-adult behavioral health: building the first state Center of Excellence for Behavioral Health and Aging
Bibliographic record
Abstract
Introduction: The behavioral health (BH) needs of older adults are unique, increasing, and, too often, poorly understood. Methods: Oregon established the first state-level center of excellence in the United States focused on the BH of older adults via a state-university-community partnership. Oregon's Center of Excellence for Behavioral Health and Aging (OCEBHA) was conceptualized by the state health authority and initially funded using a block grant from the Substance Abuse and Mental Health Services Administration. Results: OCEBHA seeks to expand the capacity of health and social programs and providers to deliver BH services for older adults with serious mental illness and substance use disorders through translational research, workforce development, and policy innovation. Conclusion: This review article describes the United States' and Oregon's BH and aging landscape, highlighting the disconnects between research evidence, clinical treatment/intervention, and policy implementation. It outlines the rationale for establishing centers like OCEBHA, which was designed to bridge these gaps. By detailing OCEBHA's structure and focus areas-translational research, workforce development, and policy innovation-the article shows how this model can help align evidence-based practices with service delivery and policy. It also offers a roadmap for other states seeking to strengthen support for older adults with BH needs.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".