Internet as a medium to seek partners among men who have sex with men

While there are reports of developing sexual relationships on the Internet (I) among MSM, there are few reports that have examined the process of developing sexual relationships on the I and comparing to that in real life (IRL). This study examines the process to provide insight into how MSM make decisions about courtship, engages in negotiations for sex, and choose sexual partners and examines the comparative sexual risks taken between I vs. IRL negotiation. This self-selected convenience sample at a national level (n=1001) of MSM recruited through the I, systematically explored the different steps, the process of courtship in a flow chart of I and IRL dating to portray the process of filtering, courtship and/or negotiation for sex. Risk behaviors in both environments are presented along with interactions that create predictable sequences or "scripts". These sequences constitute 'filtering' and 'sexual positioning'. Differences between I & IRL suggest discussion of HIV/STD status to have consistent differences for all variables except 'unprotected sex' meaning no condom use. There was more communication on the I in regards to self revealing information or variables relating to reducing risks which enable 'filtering' (including serosorting). Data indicate more steps in the I process, providing more complex, multiple steps to filter and position with regard not only to HIV/STD risk but also to negotiate position for complementary sexual interest. The study established a pattern of MSM's courtships or negotiation for sex and a pattern of acquisition, and more I negotiation. Data suggest negotiation opportunities which could lend to intervention to advise people how to negotiate safely. ^ Previous studies have reviewed MSM and drug use. This is a study to review the process of drug use associated with sexual behavior regarding the Internet (I) and in real life (IRL) using a self-selected, convenience sample of MSM (n=1001) recruited nation-wide through the Internet. Data on MSM and drugs illustrate the Internet being used as a tool to filter for drug use among MSM. MSM's drug use in both environments highlights the use of sexual performance drugs with an IRL pursuit of intimacy or negotiation for sex. IRL encounters were more likely to involve drug use (both recreational and sexual performance-enhancing) than Internet encounters. This may be due to more IRL meetings occurring at bars, clubs or parties where drug use is a norm. Compared with IRL, the Internet may provide a venue for persons who do not want to use drugs to select partners with similar attitudes. This suggests that filtering may be occurring as part of the internet negotiation. Data indicated that IRL persons get drunk/high before having sex in past 60 days significantly more often than Internet participants. Age did not alter the pattern of results. Thus drug filtering is really not recreational drug filtering or selecting for PNP, but appears to be situationally-based. Thus, it should perhaps be seen as another form of filtering to select drug-free partners, rather than using the Internet to specifically recruit and interact with other recreational drug users. ^

NONLINEAR TIME SERIES/SYSTEM ANALYSIS (STATE-SPACE, DYNAMICS, INTERVENTION, RESILIENCE, KALMAN)

A discussion of nonlinear dynamics, demonstrated by the familiar automobile, is followed by the development of a systematic method of analysis of a possibly nonlinear time series using difference equations in the general state-space format. This format allows recursive state-dependent parameter estimation after each observation thereby revealing the dynamics inherent in the system in combination with random external perturbations.^ The one-step ahead prediction errors at each time period, transformed to have constant variance, and the estimated parametric sequences provide the information to (1) formally test whether time series observations y(,t) are some linear function of random errors (ELEM)(,s), for some t and s, or whether the series would more appropriately be described by a nonlinear model such as bilinear, exponential, threshold, etc., (2) formally test whether a statistically significant change has occurred in structure/level either historically or as it occurs, (3) forecast nonlinear system with a new and innovative (but very old numerical) technique utilizing rational functions to extrapolate individual parameters as smooth functions of time which are then combined to obtain the forecast of y and (4) suggest a measure of resilience, i.e. how much perturbation a structure/level can tolerate, whether internal or external to the system, and remain statistically unchanged. Although similar to one-step control, this provides a less rigid way to think about changes affecting social systems.^ Applications consisting of the analysis of some familiar and some simulated series demonstrate the procedure. Empirical results suggest that this state-space or modified augmented Kalman filter may provide interesting ways to identify particular kinds of nonlinearities as they occur in structural change via the state trajectory.^ A computational flow-chart detailing computations and software input and output is provided in the body of the text. IBM Advanced BASIC program listings to accomplish most of the analysis are provided in the appendix. ^