Patterns of reasoning and decision making about condom use by urban college students

Abstract

HIV infection rates are rapidly increasing among young heterosexuals, making it increasingly important to understand how these individuals make decisions regarding risk in sexual encounters. Our objective in this study was to characterize young adults’ safer sex behaviour and associate this behaviour with patterns of reasoning, using cognitive, information processing methods to understand the process of sexual risk taking. Sixty urban college students from NYC maintained diaries for two weeks and then were interviewed regarding lifetime condom use and sexual history. Using cognitive analysis, we characterized four patterns of condom use behaviour: consistent condom use (35.0%), inconsistent condom use (16.7%), shifting from consistent to inconsistent condom use (35.0%), and shifting from inconsistent to consistent condom use (13.3%).

Directionality of reasoning (i.e. data-driven and hypothesis-driven reasoning) was analysed in the explanations provided for condom use decisions. The consistent and inconsistent patterns of condom use were associated with data-driven heuristic reasoning, where behaviour becomes automated and is associated with a high level of confidence in one's judgment. In the other two patterns, the shift in behaviour was due to a significant event that caused a change in type of reasoning to explanation-based reasoning, reflecting feelings of uncertainty and willingness to evaluate their decisions. We discuss these results within the framework of identifying potentially high-risk groups (e.g. heterosexual young adults) as well as intervention strategies for risk reduction. Further, our findings not only identify different patterns of condom use behaviour, but our investigation of the cognitive process of decision-making characterizes the conditions under which such behaviour and reasoning change.

Acknowledgments

This research was supported in part by a NIMH Grant R01 MH65851 to Vimla L. Patel. The authors thank Alla Keselman, David Dean, Eric Smith, Megan McCrudden, Giovanna Rodriguez, and Fuerza Fraga for their help with recruitment, and various stages of data collection and analyses.