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Avoiding disease and acquiring resources have been recurrent challenges throughout human evolution. These abilities are particularly relevant to mate preferences, as pathogens and resources can both be transferred between mates and to mutual offspring. Based on 689 participants’ attractiveness ratings of manipulated online dating profiles, we tested whether pathogen and resource concerns predicted revealed mate preferences for facial attractiveness, facial sexual dimorphism, and perceived intelligence and also whether these different trait preferences were intercorrelated. Supporting our predictions, pathogen concerns positively predicted men and women’s preferences for facial attractiveness and men’s preference for facial femininity, whereas women’s resource concerns negatively predicted their preference for facial masculinity. Unexpectedly, neither pathogen nor resource concerns predicted preference for greater perceived intelligence. Further, preference for perceived intelligence was negatively correlated with preference for facial attractiveness, which was positively correlated with preference for facial sexual dimorphism. These findings suggest that facial attributes are used in mate assessment as cues of health and likelihood of resource provisioning, whereas intelligence may primarily be used as a cue of other, distinct qualities.

This study investigated gender differences in parental monitoring and sensitivity toward parents and the extent to which these measures can influence children's mate preferences. In 2 samples (the United States and the Netherlands), females reported experiencing higher levels of parental monitoring and being more sensitive toward their parents' feelings regarding mate choice (e.g., being more upset if their parents did not approve of their partner) than males. Individuals displaying higher sensitivity toward parents rated parental investment and cooperation, and to a lesser extent genetic quality, as being more important in a potential mate than individuals lower in sensitivity toward parents. The level of parental monitoring did not significantly predict the importance of parental investment and cooperation or genetic quality in a potential mate.

The present research examined how a preference for influencing the mate choice of one's offspring is associated with opposition to out-group mating among parents from three ethnic groups in the Mexican state of Oaxaca: mestizos (people of mixed descent, n = 103), indigenous Mixtecs (n = 65), and blacks (n = 35). Nearly all of the men in this study were farmworkers or fishermen. Overall, the level of preferred parental influence on mate choice was higher than in Western populations, but lower than in Asian populations. Only among the Mixtecs were fathers more in favor of parental influence on the mate choice of children than mothers were. As predicted, opposition to out-group mating was an important predictor of preferred parental influence on mate choice, more so among fathers than among mothers, especially in the mestizo group-the group with the highest status. In addition, women, and especially mestizo women, expressed more opposition to out-group mating than men did. © 2012 Springer Science + Business Media, LLC.

In his commentary on the taxonomy of health systems and networks that we originally developed in Bazzoli et al. (1999) and updated through Dubbs et al. (2004), Luke raises various concerns about the underlying concepts, measures, and approaches we used to develop our classification scheme. His primary concerns are: (1) a bias in system assignment occurs because the taxonomy categories capture multimarket configurations of systems rather than centralization per se; (2) hospital service configuration data do not provide relevant information about the locus of health system/network decision making; (3) the conceptual framework used may not be relevant for health networks; and (4) measurement error exists in certain variables. We address each of these issues in this rejoinder. Before commenting on these points, we note that our primary purpose for developing the taxonomy in the late 1990s was to examine the structure and strategy of health networks and systems as the American Hospital Association (AHA) and others defined them. The AHA had a long history of tracking these organizations, collecting information on multihospital systems starting in the mid-1970s and on health networks beginning in 1993. Over time, the objectives and structure of these organizations changed in response to changing market imperatives. Most notable was the movement in the mid-1990s to develop organized delivery systems in anticipation of the Clinton administration's Health Security Act of 1994 and in response to the growing belief that capitated contracting between providers and health plans would become common. There was widespread acknowledgement among researchers and the industry that the organizations tracked by the AHA were growing more heterogeneous, and this led to concern that health services research that attempted to measure a system or network effect on hospital behavior would provide misleading information given the diversity of these organizations. This sparked our interest, and that of the Agency for Healthcare Research and Quality, which funded our research, in developing tools for researchers, the hospital industry, and policy makers so they could identify and examine more finely grained, homogenous groups of health networks and systems.

A number of years ago a taxonomy of health networks and systems was created, using data from the mid-1990s, for the purpose of defining the structures and strategies of newly emerging health care organizations and identifying classes of health care organizations that share common features (Bazzoli et al. 1999). Since that time, the taxonomy has become a valuable tool not only for policymakers and practitioners, but also for health services researchers who have used it to address the performance of groups of health organizations that share similar characteristics (Bazzoli et al. 2000; Bazzoli et al. 2001; Carey 2003; Dubbs 2003; Lee Alexander and Bazzoli 2003; Rosko and Proenca 2003; Shortell, Bazzoli et al. 2000). The health care world, however, has changed a great deal since the mid-1990s and in order for a taxonomy of health networks and systems to continue to be useful, it must be periodically updated to assure it appropriately depicts the spectrum of current organizational configurations. When the initial taxonomy of health networks and systems was created, it reflected organizations' anticipation of continued strong momentum toward service and financial integration created by the prospects for national health care reform in 1994. By 1998, however, this momentum had slowed substantially. In addition, by the late 1990s, capitation trends had failed to unfold as anticipated, consumer backlash against managed care had intensified, merger activity had decreased, and vertical disintegration had begun to occur (Bazzoli et al. 2001; Lesser and Ginsburg 2000; Shortell, Gillies et al. 2000b). As organizational configurations shifted and members of health networks/systems realigned, the major categories of organizations identified in the taxonomy may have changed. As a first step toward understanding these changes, a longitudinal study of organizational trends from 1994 to 1998 was conducted using the original taxonomy categorization scheme (Bazzoli et al. 2001). In this work, significant shifts were found in the numbers of observations appearing in each organizational category, as well as a general shift whereby observations tended to go from the centralized categories to the more moderately centralized categories. Now, as a next step, the work described in this paper explores the possibility that, over time, there could be additional changes in organizational structure whereby 1998 networks/systems actually cluster into entirely new categories than they did five years ago. The objectives of this paper are to revisit the taxonomy of health networks and systems in order to: (a) assess how the original cluster categories of hospital-led health networks and systems have changed over time; (b) identify any new patterns of cluster configurations; and (c) demonstrate how additional data can be used to refine and enhance the taxonomy measures.