Randomized Controlled Trials (RCTs)

Study used mixed-effects repeated measures analysis to examine the data, with the subject as a random variable and the assessment points (baseline, end of acute treatment, end of 1 year of maintenance treatment, and end of 2 years of maintenance treatment) as fixed variables. Acute treatment assignment and acute treatment by assessment time point interaction were also included in the model. The covariance matrix was unstructured. Vocational functioning scores were also examined by gender.

The measures included several standardized instruments measuring addiction severity, current use of substances, reading ability, and life experiences. Employment information was captured using the Timeline Follow Back for Employment (TLFB-E). Two variables served as primary outcomes: 1) time (number of days) to employment, either a new taxed job or enrollment in a job-training program within the follow-up period, and 2) total hours either working or enrolled in a job-training program within the time period. Time to employment and time to enrollment in a job-training program were tested using the Cox Regression procedure. Analyses addressing the number of participants reporting employment or enrollment in a training program were conducted using Chi-square tests. Analyses focused on hours spent working or in training were conducted using an analysis of variance (ANOVA). Comparisons based on repeated measures over time were tested using multivariate repeated measures analyses of variance (MANOVA).

Interviews were used to gain information about demographics, employment histories, income sources and amounts, clinical symptoms and other relevant information at the beginning of the study and then at 6 month intervals for 24 months. A variety of employment data was gathered such as hours worked, wages, job duties, benefits. Psychiatric symptoms were assessed using the PANSS a semi structured rating scale.
Bivariate analysis were conducted to test for significant differences in study conditions. Two vocational outcome variables were computed for every month of the 24 month follow up period: competitive employment and work for 40 or more hours in a single month. The independent variable was the local unemployment rate for the geographic area surrounding each program. Some demographic and clinical covariates that should be included in multivariate models predicting employment were excluded due to their high correlation with other variables.
Unemployment rates over times were inspected for each of the seven states in which study sites were located. Then hierarchical random regression analysis using time varying and fixed covariates were used to determine the effect of unemployment rate on each of the two employment outcomes. Random effects logistic regression modeling, addressed issues found in longitudinal multi-site data such as missing observations, fixed versus time varying covariates and more.

Using intent to treat analyses and two tailed tests with significance set at p less than or equal to .05, researchers compared rates of competitive employment, as well as number of weeks , day and hours worked; gross wages earned from all sources; and gross wages earned from competitive jobs.

Data was collected using the Addiction Severity Index (ASI) and a Vocational Motivational Assessment Checklist (VMAC). The VMAC measures the behavioral actions to obtain employment and a person's perceived motivation to obtain a job. Both were administered to get baseline and 6 months later. The VMAC was also completed on a biweekly basis over the 12-week intervention period.

Assessments were completed at baseline and at the six-month follow up.

Data collection consisted of chart review protocol for collecting comorbidity data used in this analysis. Interview assessments with EIDP participants elicited information regarding demographic characteristics, employment history, and other relevant information at the time of study enrollment (baseline) and at 6-month follow-up intervals for 24 months. Vocational data were collected weekly and included job tenure, hours worked, earnings, job characteristics, and workplace disclosure of psychiatric disability. Data analysis included ordinary least-squares regression for the multivariate analysis of interval-level dependent variables and logistic regression for dichotomous outcomes.

Data were collected at intake and every 6 months for the entire 24-month period using the Positive and Negative Syndrome Scale. In addition, two sites used the DSM-IV at study enrollment, while other sites extracted information from clinical records.

Public disability income beneficiary status was monitored bi-annually, as well as "self-rated level of functioning, marital status, and educational attainment" (p. 507). Sites also collected labor force data, such as weekly earnings and hours worked, job duties, eligibility for health and sick leave benefits, and level of integration in the workplace. Sites reported on amount of vocational and psychiatric services received each month.

Interviews were used to gain information about demographics, employment histories, income sources and amounts, clinical symptoms and other relevant information at the beginning of the study and then at 6 month intervals for 24 months. A variety of employment data was gathered such as hours worked, wages, job duties, benefits. Psychiatric symptoms were assessed using the PANSS a semi structured rating scale.
Bivariate analysis were conducted to test for significant differences in study conditions. Two vocational outcome variables were computed for every month of the 24 month follow up period: competitive employment and work for 40 or more hours in a single month. The independent variable was the local unemployment rate for the geographic area surrounding each program. Some demographic and clinical covariates that should be included in multivariate models predicting employment were excluded due to their high correlation with other variables.
Unemployment rates over times were inspected for each of the seven counties in which study sites were located. Then hierarchical random regression analysis using time varying and fixed covariates were used to determine the effect of unemployment rate on each of the two employment outcomes. Random effects logistic regression modeling, addressed issues found in longitudinal multi-site data such as missing observations, fixed versus time varying covariates and more.

Dependent variables were two vocational outcome measures: competitive employment and work for 40 or more hours in a single month. All experimental programs integrated psychiatric treatment and vocational services. An Employment Intervention Demonstration Program measure was developed to operationalize the level of integration. In addition, the effects of the number of hours of psychiatric and vocational services were examined separately. A running cumulative total of service hours for each service was calculated monthly for each of the 24 months of study. Dichotomous variables were used for gender, minority status, and education. Age was measured in 10 year intervals. The structured Clinical Interview for DSM IV was administered at two sites while case records by treating psychiatrist were used at the other sites. Level of functioning was self rated. Receipt of public assistance was reported by the subjects. An evaluation of the Employment Intervention Demonstration Program's protocol found good to excellent validity and reliability on these measures. Of the 1,273 participants, 65% completed five interviews. Those participants were compared to others regarding model covariates. The only significant differences were gender and age. After calculation and inspection of frequency distributions and zero order relationships, outcome were visually inspected. Then random-effects logistic regression modeling, was used to address hypotheses at the multivariate level. All models included: demographics, clinical factors, time and study site.