A computer-based interactive multimedia program to reduce HIV transmission for women with intellectual disability.

BACKGROUND: Despite recent recognition of the need for preventive sexual health materials for people with intellectual disability (ID), there have been remarkably few health-based interventions designed for people with mild to moderate ID. The purpose of this study was to evaluate the effects of a computer-based interactive multimedia (CBIM) program to teach HIV/AIDS knowledge, skills and decision making. METHODS: Twenty-five women with mild to moderate ID evaluated the program. The study used a quasi-experimental within-subjects design to assess the efficacy of the CBIM program. Research participants completed five qualitative and quantitative instruments that assessed HIV knowledge, and decision-making skills regarding HIV prevention practices and condom application skills (i.e. demonstration of skills opening a condom and putting it on a model penis). In addition, 18 service providers who work with women with ID reviewed the program and completed a demographics questionnaire and a professional customer satisfaction survey. RESULTS: Women with ID showed statistically significant increases from pre-test to post-test in all knowledge and skill domains. Furthermore, the statistical gains were accompanied by medium to large effect sizes. Overall, service providers rated the program highly on several outcome measures (stimulation, relevance and usability). CONCLUSIONS: The results of this study indicate the CBIM program was effective in increasing HIV/AIDS knowledge and skills among women with ID, who live both semi-independently and independently, in a single-session intervention. As the CBIM program is not dependent on staff for instructional delivery, it is a highly efficient teaching tool; and CBIM is an efficacious means to provide behavioural health content, compensating for the dearth of available health promotion materials for people with ID. As such, it has a potential for broad distribution and implementation by medical practitioners, and public health offices. People with ID are part of our society, yet continue to be overlooked, particularly in the area of health promotion. Special tools need to be developed in order to address the health disparities experienced by people with ID.

Role of multiple resources in predicting time-sharing efficiency: evaluation of three workload models in a multiple-task setting.

The goal of our study was to assess the validity of the assumptions underlying three prominent workload models: the Time-Line Analysis and Prediction workload model (Parks & Boucek, 1989), the VACP workload model (Aldrich, Szabo, & Bierbaum, 1989), and the W/INDEX model (North & Riley, 1989). Sixteen subjects flew a low-fidelity flight simulation. Subjects were required to perform a two-axis tracking task, a concurrent visual-monitoring task, and a discrete decision task. The decision task had 16 variations defined by two levels on each of the following dimensions: input modality (visual vs. auditory), processing code (spatial vs. verbal), difficulty (easy vs. hard), and response modality (manual vs. voice). Dual-task costs were found only for the tracking task. The tracking data were then analyzed using two approaches: a traditional analysis of variance (ANOVA) and a correlational analysis of tracking performance versus model predictions. The ANOVA revealed that performance on the tracking task was better when the concurrent decision task was responded to vocally and was easy. Input modality and processing code of the concurrent decision task had no significant effect on tracking performance. The correlational analysis was used to evaluate each of the three models, to determine what features were responsible for improving the models' fit, and to compare their performance with a pure time-line model that makes no multiple-resource assumptions. All three models did a good job of predicting variance between experimental conditions, accounting for between 56% and 84% of the variance in our data and between 10% and 40% of an earlier data set. Different features of each model that affect the fit are then discussed. We conclude that it is important for models to retain a multiple-resource coding, although the best features of that coding remain to be determined. Coding tasks by their demand level appears to be less critical.