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To increase the supply of mental health researchers from ethnic minority groups, in 1981 the National Institute of Mental Health initiated the Minority Access to Research Careers (MARC) program which was renamed Career Opportunities in Research (COR). In this paper, the Program Director of the MARC/COR Program at California State University, Long Beach, focuses on the process of operating this program day-to-day, rather than on the program structure. Lessons learned are summarized as follows: (1) Students change their minds a lot or do not know what they want in the first place; (2) Student motivation and morale waxes and wanes; (3) No single model for mentoring exists; (4) The task of matching students with mentors is not easy; (5) Not all mentors are created equal; (6) Program management is a daily task; (7) Getting students into graduate schools is not always easy; (8) Getting out of graduate school may be harder than getting in. Benefits of the program are discussed under "The Big Payoff: What Makes It all Worthwhile." (EMK)

Examines how alcohol expectancies of women are related to how they cope with a hypothetical male significant other problem drinker. Results reveal that women with higher expectancies of positive effects of alcohol were more likely to enable or support the problem drinker, but were less likely to use physical withdrawal or support sobriety. (Contains 19 references and 1 table.) (GCP)

Reducing absences is the most readily available strategy that high schools have for reducing the number of dropouts. A sample of approximately 2,000 students who entered three Oregon urban high schools in the Fall of 1983 or 1984 was studied in order to establish criteria for identifying students who were at-risk of not graduating. The group was divided into "at-risk" and "not at-risk" groups on the basis of freshman year Grade Point Average (GPA), and number of days absent. "At-risk" students were identified as those having a GPA below 1.6, and 15 or more days absent. Both groups completed questionnaires concerning their attitudes, expectations, and social and family interactions as freshmen, when they were freshman and again in December 1986. Findings from analysis of the survey, GPA, and absenteeism information include the following: (1) responses to the survey questionnaire indicated more similarities than differences between the "at-risk" and "not at-risk"; (2) two-thirds of the eventual dropouts were identified by 10 days or more of absence in their freshman year; and (3) a slight improvement in accuracy of prediction could be obtained by using the average number of class absences, using a cut-off value of an average of four or more class periods absent. Twelve tables of statistical data and a one-page list of references are included. A copy of the student questionnaire, and a list of programs for at-risk students are included in the appendices. (FMW)

Next-generation science and exploration systems will employ new observation strategies that will use multiple sensors in a dynamic environment to provide high quality monitoring, self-consistent analyses and informed decision making. The Science Goal Monitor (SGM) is a prototype software tool being developed to explore the nature of automation necessary to enable dynamic observing of earth phenomenon. The tools being developed in SGM improve our ability to autonomously monitor multiple independent sensors and coordinate reactions to better observe the dynamic phenomena. The SGM system enables users to specify events of interest and how to react when an event is detected. The system monitors streams of data to identify occurrences of the key events previously specified by the scientist/user. When an event occurs, the system autonomously coordinates the execution of the users desired reactions between different sensors. The information can be used to rapidly respond to a variety of fast temporal events. Investigators will no longer have to rely on after-the-fact data analysis to determine what happened. Our paper describes a series of prototype demonstrations that we have developed using SGM and NASA's Earth Observing-1 (EO-1) satellite and Earth Observing Systems Aqua/Terra spacecrafts MODIS instrument. Our demonstrations show the promise of coordinating data from different sources, analyzing the data for a relevant event, autonomously updating and rapidly obtaining a follow-on relevant image. SGM is being used to investigate forest fires, floods and volcanic eruptions. We are now identifying new earth science scenarios that will have more complex SGM reasoning. By developing and testing a prototype in an operational environment, we are also establishing and gathering metrics to gauge the success of automating science campaigns.

Infusion of automation technologies into NASA s future missions will be essential because of the need to: (1) effectively handle an exponentially increasing volume of scientific data, (2) successfully meet dynamic, opportunistic scientific goals and objectives, and (3) substantially reduce mission operations staff and costs. While much effort has gone into automating routine spacecraft operations to reduce human workload and hence costs, applying intelligent automation to the science side, i.e., science data acquisition, data analysis and reactions to that data analysis in a timely and still scientifically valid manner, has been relatively under-emphasized. In order to introduce science driven automation in missions, we must be able to: capture and interpret the science goals of observing programs, represent those goals in machine interpretable language; and allow spacecrafts onboard systems to autonomously react to the scientist's goals. In short, we must teach our platforms to dynamically understand, recognize, and react to the scientists goals. The Science Goal Monitor (SGM) project at NASA Goddard Space Flight Center is a prototype software tool being developed to determine the best strategies for implementing science goal driven automation in missions. The tools being developed in SGM improve the ability to monitor and react to the changing status of scientific events. The SGM system enables scientists to specify what to look for and how to react in descriptive rather than technical terms. The system monitors streams of science data to identify occurrences of key events previously specified by the scientist. When an event occurs, the system autonomously coordinates the execution of the scientist s desired reactions. Through SGM, we will improve om understanding about the capabilities needed onboard for success, develop metrics to understand the potential increase in science returns, and develop an operational prototype so that the perceived risks associated with increased use of automation can be reduced.

Infusion of automation technologies into NASA s future missions will be essential not only to achieve substantial reduction in mission operations staff and costs, but also in order to both effectively handle an exponentially increasing volume of scientific data and to successfully meet dynamic, opportunistic scientific goals and objectives. Current spacecraft operations cannot respond to science driven events, such as intrinsically variable or short-lived phenomena in a timely manner. For such investigations, we must teach our platforms to dynamically understand, recognize, and react to the scientists goals. While much effort has gone into automating routine spacecraft operations to reduce human workload and hence costs, applying intelligent automation to the science side, i.e., science data acquisition, data analysis and reactions to that data analysis in a timely and still scientifically valid manner, has been relatively under-emphasized.