The outcome of a training programme (RESPECT) on staff’s attitudes towards causes and management of aggression in a Regional Referral Hospital of Northern Uganda

Introduction Occupational violence has been demonstrated to impact negatively on the well‐being of nurses and patients. Staff attitudes towards causes and management of patients’ aggression influence their practice. Training is likely to influence attitudes towards aggression; however, Uganda's health system lacks adequate resources to provide aggression management training for staff. Aim To assess the impact of a training programme (RESPECT) on staff attitudes towards causes and management of patient's aggression in a Ugandan hospital. Methods This study used a mixed‐methods convergent design. A convenience sample of nurses and support staff employed in the psychiatric ward and other services across the hospital (N = 90) completed the Management of Aggression and Violence Attitude Scale (MAVAS) pre‐ and post‐training. The views of a smaller sample (n = 35) were captured via interviews and focus groups and analysed using thematic analysis. Results Participants reported greater agreement with patients’ physical and social environment (external and situational causative models) as factors influencing patient's aggression. Qualitative findings substantiated the results identified in the survey. Attitudes towards seclusion, restraint and medication remained unchanged. Discussion and implications for practice RESPECT has the potential to change staff attitudes towards aggression in the short term. Further research is needed to investigate long‐term effects and impact on incidents of aggression

Effects of Externally Rated Job Demand and Control on Depression Diagnosis Claims in an Industrial Cohort

This study examined whether externally rated job demand and control were associated with depression diagnosis claims in a heavy industrial cohort. The retrospective cohort sample consisted of 7,566 hourly workers aged 18–64 years who were actively employed at 11 US plants between January 1, 1996, and December 31, 2003, and free of depression diagnosis claims during an initial 2-year run-in period. Logistic regression analysis was used to model the effect of tertiles of demand and control exposure on depression diagnosis claims. Demand had a significant positive association with depression diagnosis claims in bivariate models and models adjusted for demographic (age, gender, race, education, job grade, tenure) and lifestyle (smoking status, body mass index, cholesterol level) variables (high demand odds ratio = 1.39, 95% confidence interval: 1.04, 1.86). Control was associated with greater risk of depression diagnosis at moderate levels in unadjusted models only (odds ratio = 1.47, 95% confidence interval: 1.12, 1.93), while low control, contrary to expectation, was not associated with depression. The effects of the externally rated demand exposure were lost with adjustment for location. This may reflect differences in measurement or classification of exposure, differences in depression diagnosis by location, or other location-specific factors

Using complex networks to model, simulate and understand the dynamics of psychotherapeutic processes: An experimental study proposal

The goal of our study was to utilize the science of complex interactions to bridge the gap in research concerning psychotherapy. In particular, we chose to focus on the use of graph theory to model and simulate the topology and dynamics of the psychotherapeutic relationship, given how in the past few years such method has been instrumental in understanding complex dynamic systems. © 2018 IEEE

The Nodes of Treatment: A Pilot Study of the Patient-Therapist Relationship Through the Theory of Complex Systems

Psychotherapy, unanimously described as a particular organized and systematic relationship between a patient and a therapist, is a real complex system. The interaction between the numerous variables belonging to the patient, the therapist and the context in which the therapeutic couple is inserted, presents auto-poietic characteristics and generates emergent qualities that, at the current state of psychotherapy research, have not been effectively addressed. The methods of machine learning are suitable for analyzing complex systems and in our opinion, at the moment, they are the most appropriate for studying the therapeutic relationship, understood as a quality emerging from patient-therapist interaction. In fact, through the use of artificial intelligence methods it is possible to construct a model of interaction between therapist and patient by integrating in it the non-linearity of information exchanges between the components of the system. The humanistic therapies vision of the patient-therapist relationship as a complex and organized interaction between the parts of a system is comparable to the networks of cellular chemical reactions described by Varela and Maturana. In these networks, which are a complex systems, what is important for maintaining the cell’s integrity and its functioning it is not the nature of every single chemical reaction but the form and dynamics of their interaction. This research is a pilot study that intends to evaluate the possibility of describing the complexity of therapeutic relationships using the methods of machine learning and complex networks, ordinarily used to study systems composed of numerous interacting variables. From this pilot study emerges that the use of graphs is certainly a valid tool for the analysis of both the psychotherapeutic sessions and the evolution of the care relationship over time. Numerous suggestions on the dynamics within the patient-therapist system emerge from the construction of a complex network useful for describing the trend of psychotherapy, which in this way can be described without losing the value of the wealth of each individual experience

Aggression or Aggressiveness?: A research hypothesis on aggression, videogames and executive functions in preschool age

The scientific evidence found in the literature shows that aggression is innate in humans, in a reactive and proactive form. This work proposes a distinction between an aggressive act and a predisposition to aggression, highlighting how this type of behavior is normative and natural in child's development. Aggressiveness reaches its peak between two and four years old and tends to decrease sensibly and permanently throughout the life. This work traces genetic and temperamental correlates of aggressiveness, highlights the relationship between aggression and decision making and explores the elements that lead to the choice of implementing aggressive behavior. Finally, we discuss the preliminary research proposal for the development of a gaming software, structured with a set of decision tasks, including measurement of reaction times, in order to detect the predisposition to chose aggressive behavior. This will enable the correlation between the collected data and the results obtained from the temperamental tests. © 2018 IEEE

RUES2 hESCs exhibit MGE-biased neuronal differentiation and muHTT-dependent defective specification hinting at SP1

RUES2 cell lines represent the first collection of isogenic human embryonic stem cells (hESCs) carrying different pathological CAG lengths in the HTT gene. However, their neuronal differentiation potential has yet to be thoroughly evaluated. Here, we report that RUES2 during ventral telencephalic differentiation is biased towards medial ganglionic eminence (MGE). We also show that HD-RUES2 cells exhibit an altered MGE transcriptional signature in addition to recapitulating known HD phenotypes, with reduced expression of the neurodevelopmental regulators NEUROD1 and BDNF and increased cleavage of synaptically enriched N-cadherin. Finally, we identified the transcription factor SP1 as a common potential detrimental co-partner of muHTT by de novo motif discovery analysis on the LGE, MGE, and cortical genes differentially expressed in HD human pluripotent stem cells in our and additional datasets. Taken together, these observations suggest a broad deleterious effect of muHTT in the early phases of neuronal development that may unfold through its altered interaction with SP1