AIDS counselling for low-risk clients

Abstract

Abstract. Th is study addresses the counselling of heterosexual men with low-risk behaviour who, voluntarily or involuntarily, take a HIV test. If such a man tests positive, the chance that he is infected can be as low as 50%. We study what information counsellors communicate to clients concerning the meaning of a positive test and whether they communicate this information in a way the client can understand. To get realistic data, one of us visited as a client 20 public health centres in Germany to take 20 counselling sessions and HIV tests. A majority of the counsellors explained that false positives do not occur, and half of the counsellors told the client that if he tests positive, it is 100% certain that he is infected with the virus. Counsellors communicated numerical information in terms of probabilities rather than absolute frequencies, became confused, and were inconsistent. Based on experimental evidence, we propose a simple method that counsellors can learn to communicte risks in a more eff ective way.

Primacy or recency effects in forming inductive categories

“…One possible explanation is that in any random order of stimuli, there are likely to be occasional sequences in which a distribution may subjectively appear to change but this shift may merely represent a temporary statistical perturbation in the selection of stimuli. Given that people are poor in their understanding of sampling or statistical inference (Fiedler, 2000;Sedlmeier & Gigerenzer, 1997), which can sometimes lead to fatal consequences for judgments (Gigerenzer, 2004;Gigerenzer, Hoffrage, & Ebert, 1998), relying on initial stimuli to establish a representation may, in some cases, improve the accuracy of a category judgment by preventing the adjustment of a representation due to a natural perturbation in the random order of stimuli.…”

Teaching Bayesian reasoning in less than two hours.

“…When information was presented in terms of probabilities, only 10% of the physicians reasoned consistently with Bayes's rule. Gigerenzer, Hoffrage, and Ebert (1998) studied how AIDS counselors explain what a low-risk client's chances are that he actually has the virus if he tests positive. As an assumed client, one of the authors visited 20 public health centers in Germany to have 20 counseling sessions and HTV tests.…”

Risikoabschätzung in der Medizin am Beispiel der Krebsfrüherkennung

“…Gesundheitsa¨mtern war die Mehrzahl der Berater fä lschlicherweise ü berzeugt, dass falsch-positive Testergebnisse nie vorkä men, und 50% der Berater, dass ein positives Testergebnis zu 100% sicher ist [18]. Vergleichbare mangelnde Kenntnis der relevanten medizinischen Forschung fand sich bei einer verdeckten Befragung mit 20 Urologen bezü glich der Zuverlä ssigkeit eines PSA-Tests: Hier gaben letztlich nur 2 von diesen 20 eine korrekte und vollsta¨ndige Auskunft [19].…”