Predicting program success for adult offenders with serious mental illness in a court ordered diversion program

ABSTRACT

Individuals with mental illnesses are disproportionately involved with the criminal justice system and are now being diverted from jails to community-based supervision. This study examines secondary data from a state hospital, mental health diversion program. Logistic regression was used to examine risk factors that best predicted successful program completion. Results indicate that non-whites are more likely to complete. Participants in supervised housing, ever been re-hospitalized, diagnosed with a depressive or psychotic disorder or who had their probation revoked were less likely to complete. Findings indicate practitioners should provide a holistic and individually oriented treatment approach for success.     

Treatment of late-life mental disorders in primary care: we can do a better job

Health care services provided to older adults today are not as effective as they should be. The quality of care for late-life mental disorders often falls short of desired standards. The growth of the elderly population makes it imperative for the health care system to address late-life mental disorders more effectively. Intervention strategies based in primary care settings show the most promise, but effectiveness will depend on solving the geriatric psychiatry workforce crisis. Collaborative care is one promising model for improving geriatric mental health care delivery in primary care. Diffusion of collaborative care into the health care system and integrating geriatric psychiatry into other models such as geriatric medical homes will require redesign of the organization and financing of primary care and psychiatry to overcome current barriers. Public policy should reflect the essential role of psychiatry in geriatrics and promote the integration of geriatric psychiatry with primary care.     

The time-varying performance of the long-run demand for money in the United States

This article investigates the issues of the stability and predictability and interest-sensitivity of money demand over 1870–1997. Two different estimation methodologies are used - random coefficient (RC) modeling and vector error correction (VEC) modeling. The former procedure allows the profiles of the coefficients to be traced over time and relaxes several restrictions routinely imposed in applied work. The results indicate that different estimation methodologies using different data periods and frequencies yield estimates of some of the coefficients of the long-run demand for money that fall within a fairly narrow range. The results also suggest that specification errors have had an important influence on the time profile of the interest elasticity of money demand and that there is a tendency for the interest elasticity to decline in absolute value as interest rates decline.