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In information technology, the confrontation of commercial demands with recently introduced techniques and methods creates working conditions characterized by stress and strain. The present paper re-analyses data gathered in a research project dating from 1989 among Dutch information technology personnel. Working from Karasek's job stress model, exploratory multiple correspondence analyses were used on the data matrix of occupations and work items. Two models were employed: one that emphasized the negative evaluations of job aspects, and one that emphasized positive evaluations. The structuring of positive evaluations of job aspects proved the most meaningful. Occupations could be distinguished into three groups, all three groups being characterized by a certain relation between workload and autonomy. One group, with an unfavourable judgement of workload, matched by insufficient autonomy, is at risk of overload. Another group, however, those of the computer specialists, report a workload level that is too slight given their degree of autonomy; this group appears to be at risk of under burdening.

Objectives: To effectively decrease occupational exposure to flour dust and related allergens, detailed information on exposure determinants and effectiveness of control measures is essential. In this paper, we use personal real-time exposure measurements to get more insight into the relationship between specific work characteristics, including the use of control measures, and (peak) exposure to flour dust. The study has three objectives: (i) identify tasks and activities related to peak exposure, (ii) identify control measures and other important exposure determinants and (iii) assess the potential impact of these control measures on the (peak) exposure to flour dust. Methods: A data set containing 82 real-time exposure measurements in combination with information from detailed observations was used to study the association between peak exposures and different tasks, activities and other determinants such as control measures. Descriptive statistics of peak exposure on job level were generated as well as information on contribution of task-specific peak exposures to time-weighted average (TWA) exposure levels. Finally, we evaluated the efficacy of a variety of control measures on task exposure by comparing exposure levels of groups of workers with and without controls. Results: In workers included in this study, >75% of TWA exposure is directly associated with peak exposures during a limited set of well-defined tasks/activities. The impact of a single task on population TWA exposure is generally limited (