Taxometric methods enable determination of whether the latent structure of a construct is dimensional or taxonic (nonarbitrary categories). Although sex as a biological category is taxonic, psychological gender differences have not been examined in this way. The taxometric methods of mean above minus below a cut, maximum eigenvalue, and latent mode were used to investigate whether gender is taxonic or dimensional. Behavioral measures of stereotyped hobbies and physiological characteristics (physical strength, anthropometric measurements) were examined for validation purposes, and were taxonic by sex. Psychological indicators included sexuality and mating (sexual attitudes and behaviors, mate selectivity, sociosexual orientation), interpersonal orientation (empathy, relational-interdependent self-construal), gender-related dispositions (masculinity, femininity, care orientation, unmitigated communion, fear of success, science inclination, Big Five personality), and intimacy (intimacy prototypes and stages, social provisions, intimacy with best friend). Constructs were with few exceptions dimensional, speaking to Spence's (1993) gender identity theory. Average differences between men and women are not under dispute, but the dimensionality of gender indicates that these differences are inappropriate for diagnosing gender-typical psychological variables on the basis of sex.

We report the development of the Translational Science Benefits Model (TSBM), a framework designed to support institutional assessment of clinical and translational research outcomes to measure clinical and community health impacts beyond bibliometric measures. The TSBM includes 30 specific and potentially measurable indicators that reflect benefits that accrue from clinical and translational science research such as products, system characteristics, or activities. Development of the TSBM was based on literature review, a modified Delphi method, and in‐house expert panel feedback. Three case studies illustrate the feasibility and face validity of the TSBM for identification of clinical and community health impacts that result from translational science activities. Future plans for the TSBM include further pilot testing and a resource library that will be freely available for evaluators, translational scientists, and academic institutions who wish to implement the TSBM framework in their own evaluation efforts.

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BackgroundTraining investigators for the rapidly developing field of implementation science requires both mentoring and scientific collaboration. Using social network descriptive analyses, visualization, and modeling, this paper presents results of an evaluation of the mentoring and collaborations fostered over time through the National Institute of Mental Health (NIMH) supported by Implementation Research Institute (IRI).MethodsData were comprised of IRI participant self-reported collaborations and mentoring relationships, measured in three annual surveys from 2012 to 2014. Network descriptive statistics, visualizations, and network statistical modeling were conducted to examine patterns of mentoring and collaboration among IRI participants and to model the relationship between mentoring and subsequent collaboration.ResultsFindings suggest that IRI is successful in forming mentoring relationships among its participants, and that these mentoring relationships are related to future scientific collaborations. Exponential random graph network models demonstrated that mentoring received in 2012 was positively and significantly related to the likelihood of having a scientific collaboration 2 years later in 2014 (p = 0.001). More specifically, mentoring was significantly related to future collaborations focusing on new research (p = 0.009), grant submissions (p = 0.003), and publications (p = 0.017). Predictions based on the network model suggest that for every additional mentoring relationships established in 2012, the likelihood of a scientific collaboration 2 years later is increased by almost 7 %.ConclusionsThese results support the importance of mentoring in implementation science specifically and team science more generally. Mentoring relationships were established quickly and early by the IRI core faculty. IRI fellows reported increasing scientific collaboration of all types over time, including starting new research, submitting new grants, presenting research results, and publishing peer-reviewed papers. Statistical network models demonstrated that mentoring was strongly and significantly related to subsequent scientific collaboration, which supported a core design principle of the IRI. Future work should establish the link between mentoring and scientific productivity. These results may be of interest to team science, as they suggest the importance of mentoring for future team collaborations, as well as illustrate the utility of network analysis for studying team characteristics and activities.Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-016-0499-y) contains supplementary material, which is available to authorized users.

The similarity of collaboration patterns across the 5 networks suggests common underlying partnership formation processes. Statistical network modeling promises to be a useful tool for understanding how public health systems such as networks and coalitions can be used to improve the nation's health.