Social Cognition and Other Determinants of Perceived Social Support in Individuals With Serious Mental Illness.

ABSTRACT: Social support provides protective effects for those with serious mental illness (SMI), but these effects may be attenuated by factors that hinder positive perceptions of support. Improved understanding of social support and its determinants may hold relevance for clinical interventions and provide avenues to promote recovery and improve functioning. The present study is a cross-sectional, correlational analysis investigating the relationships among social cognition (Mayer-Salovey-Caruso Emotional Intelligence Test-Managing Emotions subscale), social anxiety (Liebowitz Social Anxiety Scale), negative symptoms (Scale for the Assessment of Negative Symptoms), and perceived social support (Interpersonal Support Evaluation List) among individuals with SMI. Data were analyzed for 59 participants from a psychosocial rehabilitation treatment study at a public, urban academic-affiliated medical center in the Midwest. Bivariate Pearson correlations revealed statistically significant associations among perceived social support and social cognition (total perceived social support, p < 0.05; appraisal support, p < 0.01), negative symptoms (appraisal support, p < 0.05), and social anxiety (self-esteem support, p < 0.05). Further, multiple linear regression revealed social cognition remained a significant predictor of perceived social support ( p < 0.05) when controlling for social anxiety and negative symptoms. Overall findings suggest a correlative relationship between social cognition and perceived social support. Conclusions, limitations, and future directions are discussed.

Therapeutic alliance in a cognitive rehabilitation programme for people with serious mental illness: A qualitative analysis.

OBJECTIVE: Therapeutic alliance is an important element of evidence-based treatments for people diagnosed with a serious mental illness. Cognitive enhancement therapy (CET) is an efficacious cognitive rehabilitation programme that emphasizes coach-participant relationships. The purpose of this study was to examine experiences of therapeutic alliance among CET participants. DESIGN: We used an exploratory qualitative approach. METHODS: Semi-structured interviews were conducted with 24 participants diagnosed with a serious mental illness. Interview transcripts were analysed by three independent coders using thematic analysis. RESULTS: Four themes related to therapeutic alliance emerged: (1) positive regard; (2) collaboration; (3) authenticity; (4) negative alliance. CONCLUSIONS: Findings suggest that therapeutic alliance is an important component of CET that bolsters the learning environment and ensures a positive group experience. Our qualitative analyses contribute greater understanding of how strong therapeutic relationships impact people with serious mental illnesses participating in evidence-based treatments beyond psychotherapy context.

Implementation and preliminary outcomes of cognitive enhancement therapy for serious mental illness in an outpatient mental health program.

BACKGROUND: Although numerous evidence-based treatments for serious mental illnesses (SMI) exist, the majority are not widely utilized in clinical settings. Cognitive enhancement therapy (CET) has been tested in randomized trials; however, knowledge regarding implementation and outcomes in naturalistic environments is scarce. AIMS: The current study is an uncontrolled, observational study describing implementation and pre- to post-outcomes of CETCleveland®, a community-based version of CET in an outpatient mental health program in the United States. METHOD: We included n = 34 diverse individuals with SMI. Data include qualitative implementation information and participant outcomes, including measures of cognition, symptoms, satisfaction and adherence. RESULTS: Overall, participant satisfaction was positive, and adherence was comparable with previous studies. Implementation information includes training, clinician and setting characteristics, and barriers and solutions. Preliminary outcomes showed that participants significantly improved in areas of neurocognition and symptoms. CONCLUSIONS: Overall, our results demonstrated successful early implementation of CET in a diverse, outpatient mental health program and provided preliminary support for the clinical utilization of CET. We hope these results will promote further access to CET and other evidence-based psychiatric rehabilitation programs in community clinics.

Graph hierarchy: a novel framework to analyse hierarchical structures in complex networks.

Trophic coherence, a measure of a graph's hierarchical organisation, has been shown to be linked to a graph's structural and dynamical aspects such as cyclicity, stability and normality. Trophic levels of vertices can reveal their functional properties, partition and rank the vertices accordingly. Trophic levels and hence trophic coherence can only be defined on graphs with basal vertices, i.e. vertices with zero in-degree. Consequently, trophic analysis of graphs had been restricted until now. In this paper we introduce a hierarchical framework which can be defined on any simple graph. Within this general framework, we develop several metrics: hierarchical levels, a generalisation of the notion of trophic levels, influence centrality, a measure of a vertex's ability to influence dynamics, and democracy coefficient, a measure of overall feedback in the system. We discuss how our generalisation relates to previous attempts and what new insights are illuminated on the topological and dynamical aspects of graphs. Finally, we show how the hierarchical structure of a network relates to the incidence rate in a SIS epidemic model and the economic insights we can gain through it.

Understanding spatial patterns in rape reporting delays.

Under-reporting and delayed reporting of rape crime are severe issues that can complicate the prosecution of perpetrators and prevent rape survivors from receiving needed support. Building on a massive database of publicly available criminal reports from two US cities, we develop a machine learning framework to predict delayed reporting of rape to help tackle this issue. Motivated by large and unexplained spatial variation in reporting delays, we build predictive models to analyse spatial, temporal and socio-economic factors that might explain this variation. Our findings suggest that we can explain a substantial proportion of the variation in rape reporting delays using only openly available data. The insights from this study can be used to motivate targeted, data-driven policies to assist vulnerable communities. For example, we find that younger rape survivors and crimes committed during holiday seasons exhibit longer delays. Our insights can thus help organizations focused on supporting survivors of sexual violence to provide their services at the right place and time. Due to the non-confidential nature of the data used in our models, even community organizations lacking access to sensitive police data can use these findings to optimize their operations.

Elementary effects analysis of factors controlling COVID-19 infections in computational simulation reveals the importance of social distancing and mask usage.

COVID-19 was declared a pandemic by the World Health Organisation (WHO) on March 11th, 2020. With half of the world's countries in lockdown as of April due to this pandemic, monitoring and understanding the spread of the virus and infection rates and how these factors relate to behavioural and societal parameters is crucial for developing control strategies. This paper aims to investigate the effectiveness of masks, social distancing, lockdown and self-isolation for reducing the spread of SARS-CoV-2 infections. Our findings from an agent-based simulation modelling showed that whilst requiring a lockdown is widely believed to be the most efficient method to quickly reduce infection numbers, the practice of social distancing and the usage of surgical masks can potentially be more effective than requiring a lockdown. Our multivariate analysis of simulation results using the Morris Elementary Effects Method suggests that if a sufficient proportion of the population uses surgical masks and follows social distancing regulations, then SARS-CoV-2 infections can be controlled without requiring a lockdown.

Quantifying people's experience during flood events with implications for hazard risk communication.

Semantic drift is a well-known concept in distributional semantics, which is used to demonstrate gradual, long-term changes in meanings and sentiments of words and is largely detectable by studying the composition of large corpora. In our previous work, which used ontological relationships between words and phrases, we established that certain kinds of semantic micro-changes can be found in social media emerging around natural hazard events, such as floods. Our previous results confirmed that semantic drift in social media can be used to for early detection of floods and to increase the volume of 'useful' geo-referenced data for event monitoring. In this work we use deep learning in order to determine whether images associated with 'semantically drifted' social media tags reflect changes in crowd navigation strategies during floods. Our results show that alternative tags can be used to differentiate naïve and experienced crowds witnessing flooding of various degrees of severity.

Resilience or robustness: identifying topological vulnerabilities in rail networks.

Many critical infrastructure systems have network structures and are under stress. Despite their national importance, the complexity of large-scale transport networks means that we do not fully understand their vulnerabilities to cascade failures. The research conducted through this paper examines the interdependent rail networks in Greater London and surrounding commuter area. We focus on the morning commuter hours, where the system is under the most demand stress. There is increasing evidence that the topological shape of the network plays an important role in dynamic cascades. Here, we examine whether the different topological measures of resilience (stability) or robustness (failure) are more appropriate for understanding poor railway performance. The results show that resilience, not robustness, has a strong correlation with the consumer experience statistics. Our results are a way of describing the complexity of cascade dynamics on networks without the involvement of detailed agent-based models, showing that cascade effects are more responsible for poor performance than failures. The network science analysis hints at pathways towards making the network structure more resilient by reducing feedback loops.

Isolating the effect of cycling on local business environments in London.

We investigate whether increasing cycling activity affects the emergence of new local businesses. Historical amenity data from OpenStreetMap is used to quantify change in shop and sustenance amenity counts. We apply an instrumental variable framework to investigate a causal relationship and to account for endogeneity in the model. Measures of cycling infrastructure serve as instruments. The impact is evaluated on the level of 4835 Lower Super Output Areas in Greater London. Our results indicate that an increase in cycling trips significantly contributes to the emergence of new local shops and businesses. Limitations regarding data quality, zero-inflation and residual spatial autocorrelation are discussed. While our findings correspond to previous investigations stating positive economic effects of cycling, we advance research in the field by providing a new dataset of unprecedented high granularity and size. Furthermore, this is the first study in cycling research looking at business amenities as a measure of economic activity. The insights from our analysis can enhance understandings of how cycling affects the development of local urban economies and may thus be used to assess and evaluate transport policies and investments. Beyond this, our study highlights the value of open data in city research.

The relationship between greenspace and the mental wellbeing of adults: A systematic review.

INTRODUCTION: The view that interacting with nature enhances mental wellbeing is commonplace, despite a dearth of evidence or even agreed definitions of 'nature'. The aim of this review was to systematically appraise the evidence for associations between greenspace and mental wellbeing, stratified by the different ways in which greenspace has been conceptualised in quantitative research. METHODS: We undertook a comprehensive database search and thorough screening of articles which included a measure of greenspace and validated mental wellbeing tool, to capture aspects of hedonic and/or eudaimonic wellbeing. Quality and risk of bias in research were assessed to create grades of evidence. We undertook detailed narrative synthesis of the 50 studies which met the review inclusion criteria, as methodological heterogeneity precluded meta-analysis. RESULTS: Results of a quality assessment and narrative synthesis suggest associations between different greenspace characteristics and mental wellbeing. We identified six ways in which greenspace was conceptualised and measured: (i) amount of local-area greenspace; (ii) greenspace type; (iii) visits to greenspace; (iv) views of greenspace; (v) greenspace accessibility; and (vi) self-reported connection to nature. There was adequate evidence for associations between the amount of local-area greenspace and life satisfaction (hedonic wellbeing), but not personal flourishing (eudaimonic wellbeing). Evidence for associations between mental wellbeing and visits to greenspace, accessibility, and types of greenspace was limited. There was inadequate evidence for associations with views of greenspace and connectedness to nature. Several studies reported variation in associations between greenspace and wellbeing by life course stage, gender, levels of physically activity or attitudes to nature. CONCLUSIONS: Greenspace has positive associations with mental wellbeing (particularly hedonic wellbeing), but the evidence is not currently sufficient or specific enough to guide planning decisions. Further studies are needed, based on dynamic measures of greenspace, reflecting access and uses of greenspace, and measures of both eudaimonic and hedonic mental wellbeing.

Concurrent Pilot Instrument Monitoring in the Automated Multi-Crew Airline Cockpit.

INTRODUCTION: Pilot instrument monitoring has been described as "inadequate," "ineffective," and "insufficient" after multicrew aircraft accidents. Regulators have called for improved instrument monitoring by flight crews, but scientific knowledge in the area is scarce. Research has tended to investigate the monitoring of individual pilots when in the pilot-flying role; very little research has looked at crew monitoring, or that of the "monitoring-pilot" role despite it being half of the apparent problem. METHODS: Eye-tracking data were collected from 17 properly constituted and current Boeing 737 crews operating in a full motion simulator. Each crew flew four realistic flight segments, with pilots swapping between the pilot-flying and pilot-monitoring roles, with and without the autopilot engaged. Analysis was performed on the 375 maneuvering-segments prior to localizer intercept. RESULTS: Autopilot engagement led to significantly less visual dwell time on the attitude director indicator (mean 212.8-47.8 s for the flying pilot and 58.5-39.8 s for the monitoring-pilot) and an associated increase on the horizontal situation indicator (18-52.5 s and 36.4-50.5 s). DISCUSSION: The flying-pilots' withdrawal of attention from the primary flight reference and increased attention to the primary navigational reference was paralleled rather than complemented by the monitoring-pilot, suggesting that monitoring vulnerabilities can be duplicated in the flight deck. Therefore it is possible that accident causes identified as "inadequate" or "insufficient" monitoring, are in fact a result of parallel monitoring.Jarvis SR. Concurrent pilot instrument monitoring in the automated multi-crew airline cockpit. Aerosp Med Hum Perform. 2017; 88(12):1100-1106.

Google Trends can improve surveillance of Type 2 diabetes.

Recent studies demonstrate that people are increasingly looking online to assess their health, with reasons varying from personal preferences and beliefs to inability to book a timely appointment with their local medical practice. Records of these activities represent a new source of data about the health of populations, but which is currently unaccounted for by disease surveillance models. This could potentially be useful as evidence of individuals' perception of bodily changes and self-diagnosis of early symptoms of an emerging disease. We make use of the Experian geodemographic Mosaic dataset in order to extract Type 2 diabetes candidate risk variables and compare their temporal relationships with the search keywords, used to describe early symptoms of the disease on Google. Our results demonstrate that Google Trends can detect early signs of diabetes by monitoring combinations of keywords, associated with searches for hypertension treatment and poor living conditions; Combined search semantics, related to obesity, how to quit smoking and improve living conditions (deprivation) can be also employed, however, may lead to less accurate results.

A cross-sectional analysis of green space prevalence and mental wellbeing in England.

BACKGROUND: With urbanisation increasing, it is important to understand how to design changing environments to promote mental wellbeing. Evidence suggests that local-area proportions of green space may be associated with happiness and life satisfaction; however, the available evidence on such associations with more broadly defined mental wellbeing in still very scarce. This study aimed to establish whether the amount of neighbourhood green space was associated with mental wellbeing. METHODS: Data were drawn from Understanding Society, a national survey of 30,900 individuals across 11,096 Census Lower-Layer Super Output Areas (LSOAs) in England, over the period 2009-2010. Measures included the multi-dimensional Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS) and LSOA proportion of green space, which was derived from the General Land Use Database (GLUD), and were analysed using linear regression, while controlling for individual, household and area-level factors. RESULTS: Those living in areas with greater proportions of green space had significantly higher mental wellbeing scores in unadjusted analyses (an expected increase of 0.17 points (95% CI 0.11, 0.23) in the SWEMWBS score for a standard deviation increase of green space). However, after adjustment for confounding by respondent sociodemographic characteristics and urban/rural location, the association was attenuated to the null (regression coefficient B = - 0.01, 95% CI -0.08, 0.05, p = 0.712). CONCLUSIONS: While the green space in an individual's local area has been shown through other research to be related to aspects of mental health such as happiness and life satisfaction, the association with multidimensional mental wellbeing is much less clear from our results. While we did not find a statistically significant association between the amount of green space in residents' local areas and mental wellbeing, further research is needed to understand whether other features of green space, such as accessibility, aesthetics or use, are important for mental wellbeing.

Predicting floods with Flickr tags.

Increasingly, user generated content (UGC) in social media postings and their associated metadata such as time and location stamps are being used to provide useful operational information during natural hazard events such as hurricanes, storms and floods. The main advantage of these new sources of data are twofold. First, in a purely additive sense, they can provide much denser geographical coverage of the hazard as compared to traditional sensor networks. Second, they provide what physical sensors are not able to do: By documenting personal observations and experiences, they directly record the impact of a hazard on the human environment. For this reason interpretation of the content (e.g., hashtags, images, text, emojis, etc) and metadata (e.g., keywords, tags, geolocation) have been a focus of much research into social media analytics. However, as choices of semantic tags in the current methods are usually reduced to the exact name or type of the event (e.g., hashtags '#Sandy' or '#flooding'), the main limitation of such approaches remains their mere nowcasting capacity. In this study we make use of polysemous tags of images posted during several recent flood events and demonstrate how such volunteered geographic data can be used to provide early warning of an event before its outbreak.

Inhibitory deficits in prepulse inhibition, sensory gating, and antisaccade eye movement in schizotypy.

Schizotypy is a term that refers to a continuum of personality characteristics, emerging from mental states ranging from organized and normal to unorganized and disordered; with the latter tending to include individuals with high schizotypal scores as well as those diagnosed with schizotypal personality disorder. Evidence from psychophysiological studies has found a relative weakness in the inhibitory functioning, including prepulse inhibition (PPI), sensory gating (SG), and antisaccade eye movement (AEM) in schizotypy and schizophrenia. As schizotypy and schizophrenia are in the same spectrum, understanding the nature of sensory and motor inhibitory weakness associated with schizotypy will optimize the prevention and intervention for both schizotypy and schizophrenia populations. This review aims at examining the deficits of sensory gating, saccade control, and prepulse inhibition in schizotypy; examining the relationship between the three measures and schizotypal symptoms and traits; examining the effect of nicotine on the three measures; and examining the relevant brain regions to the three measures. We searched multiple databases (such as MEDLINE, Pubmed, PsychINFO, Google Scholar) using combinations of the keywords: schizotypy, schizotypal personality disorder, prepulse inhibition, sensory gating and antisaccade for articles published in English since 1980. We found that three measures (SG, PPI and AEM) are associated with major schizotypal symptoms, suggesting that three measures could be used to predict the disease etiology and prognosis. Secondly, the three measures are modulated by nicotine administration at a certain level, providing a potential tool to study the role of nicotine in the cognition and symptom improvement in schizotypy. Thirdly, brain-imaging studies have localized activity in brain regions associated with sensory gating, saccade control, and prepulse inhibition, narrowing the search for brain regions to target for the treatment and prevention of schizotypy. Overall, the three measures are suggested to be a valuable tool to study the inhibitory deficits in schizotypy, and maybe used as a tool for the prevention and treatment of schizotypy as well.

Predicting the impact of urban flooding using open data.

This paper aims to explore whether there is a relationship between search patterns for flood risk information on the Web and how badly localities have been affected by flood events. We hypothesize that localities where people stay more actively informed about potential flooding experience less negative impact than localities where people make less effort to be informed. Being informed, of course, does not hold the waters back; however, it may stimulate (or serve as an indicator of) such resilient behaviours as timely use of sandbags, relocation of possessions from basements to upper floors and/or temporary evacuation from flooded homes to alternative accommodation. We make use of open data to test this relationship empirically. Our results demonstrate that although aggregated Web search reflects average rainfall patterns, its eigenvectors predominantly consist of locations with similar flood impacts during 2014-2015. These results are also consistent with statistically significant correlations of Web search eigenvectors with flood warning and incident reporting datasets.

Contribution of smoking during pregnancy to inequalities in stillbirth and infant death in Scotland 1994-2003: retrospective population based study using hospital maternity records.

OBJECTIVE: To quantify the contribution of smoking during pregnancy to social inequalities in stillbirth and infant death. DESIGN: Population based retrospective cohort study. SETTING: Scottish hospitals between 1994 and 2003. PARTICIPANTS: Records of 529 317 singleton live births and 2699 stillbirths delivered at 24-44 weeks' gestation in Scotland from 1994 to 2003. MAIN OUTCOME MEASURES: Rates of stillbirth and infant, neonatal, and post-neonatal death for each deprivation category (fifths of postcode sector Carstairs-Morris scores); contribution of smoking during pregnancy ("no," "yes," or "not known") in explaining social inequalities in these outcomes. RESULTS: The stillbirth rate increased from 3.8 per 1000 in the least deprived group to 5.9 per 1000 in the most deprived group. For infant deaths, the rate increased from 3.2 per 1000 in the least deprived group to 5.4 per 1000 in the most deprived group. Stillbirths were 56% more likely (odds ratio 1.56, 95% confidence interval 1.38 to 1.77) and infant deaths were 72% more likely (1.72, 1.50 to 1.97) in the most deprived compared with the least deprived category. Smoking during pregnancy accounted for 38% of the inequality in stillbirths and 31% of the inequality in infant deaths. CONCLUSIONS: Both tackling smoking during pregnancy and reducing infants' exposure to tobacco smoke in the postnatal environment may help to reduce stillbirths and infant deaths overall and to reduce the socioeconomic inequalities in stillbirths and infant deaths perhaps by as much as 30-40%. However, action on smoking on its own is unlikely to be sufficient and other measures to improve the social circumstances, social support, and health of mothers and infants are needed.

Centile charts for birthweight for gestational age for Scottish singleton births.

BACKGROUND: Centile charts of birthweight for gestational age are used to identify low birthweight babies. The charts currently used in Scotland are based on data from the 1970s and require updating given changes in birthweight and in the measurement of gestational age since then. METHODS: Routinely collected data of 100,133 singleton births occurring in Scotland from 1998-2003 were used to construct new centile charts using the LMS method. RESULTS: Centile charts for birthweight for sex and parity groupings were constructed for singleton birth and compared to existing charts used in Scottish hospitals. CONCLUSION: Mean birthweight has been shown to have increased over recent decades. The differences shown between the new and currently used centiles confirm the need for more up-to-date centiles for birthweight for gestational age.

Intrauterine growth and cerebral palsy in twins: a European multicenter study.

Population-based studies in twins have been of insufficient size to explore the relationship between risk of cerebral palsy and intrauterine growth. Earlier studies in singletons have suggested an optimum size at birth for minimum cerebral palsy risk between the 75th and 90th percentiles of weight for gestational age. We aggregated data from nine European cerebral palsy registers for 1976 to 1990. Using sex-specific fetal growth standards for twins, a z score of weight-for-gestation was derived for each of the 373 twin cases. The rates of cerebral palsy in each z-score band were compared to the rate in the a priori reference band of 0.67 to less than 1.28 (equivalent to the 75th to less than 90th percentiles). In twins born at 32 weeks' gestation or more (92% of all twins), cerebral palsy rates were higher for both light and heavy-for-gestation babies compared to an optimum (i.e., minimum risk) in the reference band. However, the rate ratio for heavy babies (90th percentile or greater) did not reach conventional (95% confidence intervals [CI]) statistical significance (rate ratios = 1.76; 90% CI 1.02-3.03). For twins born at less than 32 weeks, the significantly higher risk for cerebral palsy was observed consistently in all z-score bands less than average compared to the reference band. This multi-center study demonstrates that for twins born at 32 weeks' gestation or more, an increased risk of cerebral palsy is associated with deviations from optimal intrauterine growth at about 1 standard deviation above mean weight, as was earlier reported for singletons. For twins born at less than 32 weeks' gestation, this pattern is only demonstrable for babies weighing below the optimum weight-for-gestation.

Cerebral palsy and intrauterine growth.

When birth weight for gestation is used as a surrogate for intrauterine growth, the prevalence of cerebral palsy varies continuously in a reversed J shape, with steep increases in the risk for infants lighter and heavier than the optimum size. Patterns of size-at-birth specific risk for cerebral palsy differ between male and female infants, as do the patterns for more severe versus milder cases. Although these excess risks with abnormal size at birth imply antenatal precursors, it is not clear whether or how intrauterine growth is involved in any of the suspected causal pathways resulting in cerebral palsy. The implication for clinicians is that serial measures of in utero growth may provide an important indicator of fetal health.