Exploring social representations of adapting to climate change using topic modeling and Bayesian networks

When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on in order to act. Social representations theory suggests how individuals and society make sense of the unfamiliar and hence how the resultant social representations (SRs) cognitively, emotionally, and actively orient people and enable communication. SRs are social constructions that emerge through individual and collective engagement with media and with everyday conversations among people. Recent developments in text analysis techniques, and in particular topic modeling, provide a potentially powerful analytical method to examine the structure and content of SRs using large samples of narrative or text. In this paper I describe the methods and results of applying topic modeling to 660 micronarratives collected from Australian academics/researchers, government employees, and members of the public in 2010-2011. The narrative fragments focused on adaptation to climate change (CC) and hence provide an example of Australian society making sense of an emerging and conflict ridden phenomena. The results of the topic modeling reflect elements of SRs of adaptation to CC that are consistent with findings in the literature as well as being reasonably robust predictors of classes of action in response to CC. Bayesian Network (BN) modeling was used to identify relationships among the topics (SR elements) and in particular to identify relationships among topics, sentiment, and action. Finally the resulting model and topic modeling results are used to highlight differences in the salience of SR elements among social groups. The approach of linking topic modeling and BN modeling offers a new and encouraging approach to analysis for ongoing research on SRs

Using consensus analysis to assess mental models about water use and management in the Crocodile River catchment, South Africa

The content, structure, and distribution of mental models can be elicited and measured using a variety of methods. In this article we explore a method for eliciting mental models within the context of water use and management in South Africa. This method is consensus analysis, a technique developed in cognitive anthropology. We used it to analyze qualitative data from semistructured interviews, pilesorts, and questionnaires to test quantitatively the degree of sharing and diversity of mental models within and across social groups. The consensus analysis method focused on comparing the mental models of two key stakeholder groups in the Crocodile River catchment in South Africa, i.e., conservationists and irrigators, to better understand the level of consensus between these groups. We specifically investigated the level of agreement regarding: (1) major water users of the Crocodile River, (2) causes of the current problems with flows in the river, (3) consequences of the river not flowing, and 4) priorities for future use. We discuss the results and examine the strengths and challenges of consensus analysis for eliciting and measuring mental models. We also evaluated the usefulness of this method in assisting natural resource managers to identify strategies for improving integrated management of water resources

Improving coverage and compliance in mass drug administration for the elimination of LF in two 'Endgame' districts in Indonesia using micronarrative surveys

Author Summary This research describes the process used to assist two districts endemic for lymphatic filariasis (LF) in Indonesia to better understand the reasons why their LF elimination programs have had suboptimal results. A novel survey design was used to collect stories about people's direct experiences with mass drug administration (MDA) for LF. These questionnaires also explored the reasons community members took or did not take the LF drugs. Following MDA in 2013, two baseline surveys in endemic communities provided insight into the district MDA programs. Together with district health officials, feasible recommendations were provided before the next MDA round in 2014. Uptake of these recommendations by the districts was high, although no additional funding was made available for programmatic changes. As a result, both districts reported significant improvements in their MDA coverage and compliance rates after the endline surveys were completed in 2015. This demonstrated the utility of the survey tool and process to impact change and improvement in MDA programs

Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years

This is the final version. Available on open access from BMJ Publishing Group via the DOI in this recordObjective: To develop and validate multivariable clinical diagnostic models to assist distinguishing between type 1 and type 2 diabetes in adults aged 18 to 50. Design: Multivariable logistic regression analysis was used to develop classification models integrating five pre-specified predictor variables, including clinical features (age of diagnosis, BMI) and clinical biomarkers (GADA and Islet Antigen 2 islet autoantibodies, Type 1 Diabetes Genetic Risk Score), to identify type 1 diabetes with rapid insulin requirement using data from existing cohorts. Setting: United Kingdom cohorts recruited from primary and secondary care. Participants: 1,352 (model development) and 582 (external validation) participants diagnosed with diabetes between the age of 18 and 50 years of white European origin. Main outcome measures: Type 1 diabetes was defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (C-peptide <200pmol/L). Type 2 diabetes was defined by either a lack of rapid insulin requirement or, where insulin treated within 3 years, retained endogenous insulin secretion (C-peptide >600pmol/L at ≥5 years diabetes duration). Model performance was assessed using area under the receiver operating characteristic curve (ROC AUC), and internal and external validation. 4 Results: Type 1 diabetes was present in 13% of participants in the development cohort. All five predictor variables were discriminative and independent predictors of type 1 diabetes (p<0.001 for all) with individual ROC AUC ranging from 0.82 to 0.85. Model performance was high: ROC AUC range 0.90 [95%CI 0.88, 0.93] (clinical features only) to 0.97 [0.96, 0.98] (all predictors) with low prediction error. Results were consistent in external validation (clinical features and GADA ROC AUC 0.93 [0.90, 0.96]). Conclusions: Clinical diagnostic models integrating clinical features with biomarkers have high accuracy for identifying type 1 diabetes with rapid insulin requirement, and could assist clinicians and researchers in accurately identifying patients with type 1 diabetes.National Institute for Health Research (NIHR)European Community FP7Oxford Hospitals Charitable FundWellcome TrustMedical Research Council (MRC

Use of patient flow analysis to improve patient visit efficiency by decreasing wait time in a primary care-based disease management programs for anticoagulation and chronic pain: a quality improvement study

BACKGROUND: Patients with chronic conditions require frequent care visits. Problems can arise during several parts of the patient visit that decrease efficiency, making it difficult to effectively care for high volumes of patients. The purpose of the study is to test a method to improve patient visit efficiency. METHODS: We used Patient Flow Analysis to identify inefficiencies in the patient visit, suggest areas for improvement, and test the effectiveness of clinic interventions. RESULTS: At baseline, the mean visit time for 93 anticoagulation clinic patient visits was 84 minutes (+/- 50 minutes) and the mean visit time for 25 chronic pain clinic patient visits was 65 minutes (+/- 21 minutes). Based on these data, we identified specific areas of inefficiency and developed interventions to decrease the mean time of the patient visit. After interventions, follow-up data found the mean visit time was reduced to 59 minutes (+/-25 minutes) for the anticoagulation clinic, a time decrease of 25 minutes (t-test 39%; p < 0.001). Mean visit time for the chronic pain clinic was reduced to 43 minutes (+/- 14 minutes) a time decrease of 22 minutes (t-test 34 %; p < 0.001). CONCLUSION: Patient Flow Analysis is an effective technique to identify inefficiencies in the patient visit and efficiently collect patient flow data. Once inefficiencies are identified they can be improved through brief interventions

Metformin is a metabolic modulator and radiosensitiser in rectal cancer

Resistance to neoadjuvant chemoradiation therapy, is a major challenge in the management of rectal cancer. Increasing evidence supports a role for altered energy metabolism in the resistance of tumours to anti-cancer therapy, suggesting that targeting tumour metabolism may have potential as a novel therapeutic strategy to boost treatment response. In this study, the impact of metformin on the radiosensitivity of colorectal cancer cells, and the potential mechanisms of action of metformin-mediated radiosensitisation were investigated. Metformin treatment was demonstrated to significantly radiosensitise both radiosensitive and radioresistant colorectal cancer cells in vitro. Transcriptomic and functional analysis demonstrated metformin-mediated alterations to energy metabolism, mitochondrial function, cell cycle distribution and progression, cell death and antioxidant levels in colorectal cancer cells. Using ex vivo models, metformin treatment significantly inhibited oxidative phosphorylation and glycolysis in treatment naïve rectal cancer biopsies, without affecting the real-time metabolic profile of non-cancer rectal tissue. Importantly, metformin treatment differentially altered the protein secretome of rectal cancer tissue when compared to non-cancer rectal tissue. Together these data highlight the potential utility of metformin as an anti-metabolic radiosensitiser in rectal cancer

Psychopathic leadership a case study of a corporate psychopath CEO

This longitudinal case study reports on a charity in the UK which gained a new CEO who was reported by two middle managers who worked in the charity, to embody (respectively) all or most of the ten characteristics within a measure of corporate psychopathy. The leadership of this CEO with a high corporate psychopathy score was reported to be so poor that the organisation was described as being one without leadership and as a lost organisation with no direction. This paper outlines the resultant characteristics of the ensuing aimlessness and lack of drive of the organisation involved. Comparisons are made to a previous CEO in the same organisation, who was reportedly an authentic, effective and transformational leader. Outcomes under the CEO with a high corporate psychopathy score were related to bullying, staff withdrawal and turnover as effective employees stayed away from and/or left the organisation. Outcomes also included a marked organisational decline in terms of revenue, employee commitment, creativity and organisational innovativeness. The paper makes a contribution to both leadership and to corporate psychopathy research as it appears to be the first reported study of a CEO with a high corporate psychopathy score

Adaptive Analysis of Locally Complex Systems in a Globally Complex World

Zambezi Valley agro-ecosystems are environmentally, economically, and institutionally variable. This variability means that it is not possible to measure everything necessary to develop a predictive understanding of them. In particular, because people and their environments are constantly changing, what was measured yesterday may change by tomorrow. Here, I describe elements of the approach that I have developed to address this problem. Called DAAWN, for Detail as and When Needed, the approach advocates an iterative and multiscaled methodology in which we first capture as broad an understanding of the system as possible and then use awareness developed at this scale to identify where to focus subsequent, more detailed, investigations. Because we cannot hope to measure or monitor everything in these complex and adaptive agro-ecosystems, the approach requires us to make judicious use of all available knowledge about the agro-ecosystem. The DAAWN approach is rooted in systems theory, but is tempered by systems and problems where boundaries are not clearly defined, where nonlinearities are the norm, and where structural and functional change is the order of the day. I describe a few of the most important data collection tools and methods that were developed to record the knowledge of local people and to observe, monitor, and measure changes in their resources. Of particular importance is the tool that I call a "spidergram." This tool, which I used extensively with village informants, symbolizes the DAAWN approach and was a major stimulus for its development. Simulation models provide another very important tool; here, I offer some examples of spatially explicit, multi-agent models. Some key findings of the research on Zambezi Valley agro-ecosystems are also briefly presented