A mixed-method exploration of functioning in Safe Schools/Healthy Students partnerships

This paper presents a mixed-method approach to measuring the functioning of Safe Schools/Healthy Students (SS/HS) Initiative partnerships. The SS/HS national evaluation team developed a survey to collect partners’ perceptions of functioning within SS/HS partnerships. Average partnership functioning scores were used to rank each site from lowest to highest. Sites with the most favorable perceptions of partnership functioning were defined as having average scores in the top 10% (n = 10) and sites with the least favorable perceptions of partnership functioning were defined as having average scores in the bottom 10% (n = 10). Qualitative data for these 20 sites were inductively open coded for emergent themes and analyzed for patterns using grounded theory approach. Six themes emerged that distinguished sites reporting the most favorable and least favorable perceptions of partnership functioning: partner engagement, facilitators, barriers, shared decision making, partnership structure, and sustainability. Sites reporting the most favorable perceptions of partnership functioning effectively utilized collaboration processes that facilitate coalition building, such as shared decision making, effective communication, and developing a clearly defined structure. Qualitative themes from this analysis provide evidence of validity for the partnership functioning scale used and illustrate distinguishing features between sites with the most favorable and least favorable perceptions of partnership functioning.     

From 'human being' to 'social subject': "unfreezing" ergonomics and the implications for understanding and intervening health-disease process

Ergonomics has been successful in increasing productivity and comfort in the work arena. It has also contributed to reducing occupational accidents. Despite this, ergonomics is frequently limited to understanding the health-disease process related to human-technology interactions, as this process is more complex than what can be understood from an ergonomic evaluation. Recognising this limit, this work ontologically and epistemologically contrasts the notions of 'human being' and 'social subject', and concludes that the study object of ergonomics, or human-technology interaction, greatly depends on social aspects that nowadays are not tackled explicitly: route (history), project, structure, agency, motivations and power. It also analyses how participatory ergonomics tacitly includes many of these aspects, including some implications that the change of notion, from 'human being' to 'social subject', brings to the understanding of the health-disease process and the reduction of associated risks during human activities.     

Outline of a sociology of decisionism

I propose an agenda for empirical research on decision, choice, decision‐makers, and decision‐making qua social facts. Given society S, group G, or field F, I make a twofold sociological proposal. First, empirically investigate the conditions under which something—call it X—is taken to be a decision or choice, or the outcome of a decision‐making process. What must X be like? What doesn't count (besides, presumably, myotatic reflexes and blushing)? Whom or what must X be done by? What can't be a decision‐maker (besides, presumably, rocks and apples)? Second, empirically investigate how decision/choice concepts are used in everyday life, politics, business, education, law, technology, and science. What are they used for? To what extent do people understand and represent themselves and others as decision‐makers? Where do decision‐centric or “decisionist” understandings succeed? These aren't armchair, theoretical, philosophical questions, but empirical ones. Decision/choice concepts’ apparent ubiquity in contemporary societies calls for a well‐thought‐out research program on their social life and uses.     

Perspectives of Adult Survivors of Child Sexual Abuse: An Exploration of the Adjustments to Self-Structure through Meaning-Making in Therapy

This critical literature review explored how adult survivors of child sexual abuse experienced adjustments to their self-structure through meaning-making in therapy. Following extensive searches of academic databases, 15 studies were identified for review. Using thematic analysis, eight themes emerged. The eight identified themes of trust, acknowledgement, evolution, acceptance, integration, congruence, relational, and agency represented distinct but interrelated components of self-structure. The findings also indicated that self-structure components may be both intra- and interrelational in nature and that movement in one theme may facilitate movement in another. While recognizing that the nature of the research captured a composite of experiences, it was found that there was a consistent movement and fluidity as to how participants arrived at an adjusted position for each of the themes. A suggested theoretical framework was developed showing the components of the self-structure impacted by meaning-making and the nature of the adjustments made. Recommendations are made regarding future research.     

Self-healing umbrella sampling: convergence and efficiency

The Self-Healing Umbrella Sampling (SHUS) algorithm is an adaptive biasing algorithm which has been proposed in Marsili et al. (J Phys Chem B 110(29):14011–14013, 2006) in order to efficiently sample a multimodal probability measure. We show that this method can be seen as a variant of the well-known Wang–Landau algorithm Wang and Landau (Phys Rev E 64:056101, 2001a; Phys Rev Lett 86(10):2050–2053, 2001b). Adapting results on the convergence of the Wang-Landau algorithm obtained in Fort et al. (Math Comput 84(295):2297–2327, 2014a), we prove the convergence of the SHUS algorithm. We also compare the two methods in terms of efficiency. We finally propose a modification of the SHUS algorithm in order to increase its efficiency, and exhibit some similarities of SHUS with the well-tempered metadynamics method Barducci et al. (Phys Rev Lett 100:020,603, 2008).     

MODELING DIFFUSION OF MULTIPLE INNOVATIONS VIA MULTILEVEL DIFFUSION CURVES: PAYOLA IN POP MUSIC RADIO

Diffusion curve analysis can estimate whether an innovation spreads endogenously (indicated by a characteristic " s -curve") or exogenously (indicated by a characteristic negative exponential curve). Current techniques for pooling information across multiple innovations require a two-stage analysis. In this paper, we develop multilevel diffusion curve analysis, which is statistically more efficient and allows for more flexible specifications than do existing methods. To substantively illustrate this technique, we use data on bribery in pop radio as an example of exogenous influence on diffusion.     

Indicators of modern health attitudes in an urban setting: An exploration of the attitude — Behavior relationship

This paper explores the components and determinants of health attitudes that have accompanied the process of modernization in Accra. Due to the numerous failures to predict behavior from unidimensional measures of attitudes, a multidimensional approach to health attitudes was used. Factor analysis of the components revealed that this multidimensional tripartite structure of health attitudes is theoretically meaningful. Both the simple correlational and multivariate analysis of responses form a sample interview survey of 1000 women show that social, structural and community factors are important determinants of modern health attitudes; and that the influence of modern health attitudes on utilization behavior is independent of the effects of education and general modernity. The implications of these for health care delivery are discussed.     

Assessing The Impact of Managed-Care on the Distribution of Length-of-Stay Using Bayesian Hierarchical Models

Hierarchical models provide a useful framework for the complexities encountered in policy-relevant research in which the impact of social programs is being assessed. Such complexities include multi-site data, censored data and over-dispersion. In this paper, Bayesian inference through Markov Chain Monte Carlo methods is used for the analysis of a complex hierarchical log-normal model that shows the impact of a managed care strategy aimed at limiting length of hospital stays. Parameters in this model allow for variability in baseline length-of-stay as well as the program effect across hospitals. The authors demonstrate elicitation and sensitivity analysis with respect to prior distributions. All calculations for the posterior and predictive distributions were obtained using the software BUGS.