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As COVID-19 vaccines are rolled out across the world, there are growing concerns about the roles that trust, belief in conspiracy theories, and spread of misinformation through social media play in impacting vaccine hesitancy. We use a nationally representative survey of 1476 adults in the UK between 12 and 18 December 2020, along with 5 focus groups conducted during the same period. Trust is a core predictor, with distrust in vaccines in general and mistrust in government raising vaccine hesitancy. Trust in health institutions and experts and perceived personal threat are vital, with focus groups revealing that COVID-19 vaccine hesitancy is driven by a misunderstanding of herd immunity as providing protection, fear of rapid vaccine development and side effects, and beliefs that the virus is man-made and used for population control. In particular, those who obtain information from relatively unregulated social media sources—such as YouTube—that have recommendations tailored by watch history, and who hold general conspiratorial beliefs, are less willing to be vaccinated. Since an increasing number of individuals use social media for gathering health information, interventions require action from governments, health officials, and social media companies. More attention needs to be devoted to helping people understand their own risks, unpacking complex concepts, and filling knowledge voids.

The relationship between electoral competitiveness and turnout forms the foundations of understanding voter behaviour. The more competitive an election, the more ballots that should be cast. This simple and longstanding association means that some form of competitiveness is now “used as an explanatory variable as a matter of course” (Denver, Hands and MacAllister, 2003:174) in the study of elections. Yet, the relationship was established in stable, two-party, majoritarian systems and focuses only on the aggregate level. Moreover, turnout is becoming increasingly difficult to predict (House of Lords, 2018; Biddle 2019) which suggests a weakening of the association. Instead, I argue that changes in the party system have altered the competitive environment, which in turn requires the revisiting of competitiveness itself.This research establishes two new conceptual terms for electoral political science. The first is system uncertainty, which represents the competitiveness of the district in which a contest is being held. The second is preference uncertainty, which is experienced at the individual level when a voter does not prefer one party over all others. Additionally, two new measurements for these concepts are constructed. The effects of these types of competitiveness on turnout are tested using British Election Study longitudinal data. The findings show that system uncertainty fosters turnout, in line with previous literature. In contrast, there is evidence that preference uncertainty fosters abstention and that the competitiveness-turnout relationship is moderated by individuals’ risk aversion. This has widespread implications for electoral behaviour literatures, and could explain why many people vote when it is irrational to do so.

Background The non-medical use of opioids has reached epidemic levels nationwide, and rural areas have been particularly affected by increasing rates of overdose mortality as well as increases in the prison population. Individuals with opioid use disorder (OUD) are at increased risk for relapse and overdose upon reentry to the community due to decreased tolerance during incarceration. It is crucial to identify barriers to substance use disorder treatment post-release from prison because treatment can be particularly difficult to access in resource-limited rural Appalachia. Methods A social ecological framework was utilized to examine barriers to community-based substance use treatment among individuals with OUD in Appalachian Kentucky following release from prison. Semi-structured qualitative interviews with 15 social service clinicians (SSCs) employed by the Department of Corrections were conducted to identify barriers at the individual, interpersonal, organizational/institutional level, community, and systems levels. Two independent coders conducted line-by-line coding to identify key themes. Results Treatment barriers were identified across the social ecological spectrum. At the individual-level, SSCs highlighted high-risk drug use and a lack of motivation. At the interpersonal level, homogenous social networks (i.e., homophilious drug-using networks) and networks with limited treatment knowledge inhibited treatment. SSC’s high case load and probation/parole officer’s limited understanding of treatment were organizational/institutional barriers. Easy access to opioids, few treatment resources, and a lack of community support for treatment were barriers at the community level. SSC’s noted system-level barriers such as lack of transportation options, cost, and uncertainty about the implementation of the Affordable Care Act. Conclusions More rural infrastructure resources as well as additional education for family networks, corrections staff, and the community at large in Appalachia are needed to address barriers to OUD treatment. Future research should examine barriers from the perspective of other key stakeholders (e.g., clients, families of clients) and test interventions to increase access to OUD treatment.

“Blue carbon” wetland vegetation has a limited freshwater requirement. One type, mangroves, utilizes less freshwater during transpiration than adjacent terrestrial ecoregions, equating to only 43% (average) to 57% (potential) of evapotranspiration ( ET ). Here, we demonstrate that comparative consumptive water use by mangrove vegetation is as much as 2905 kL H 2 O ha −1  year −1 less than adjacent ecoregions with E c -to- ET ratios of 47–70%. Lower porewater salinity would, however, increase mangrove E c -to- ET ratios by affecting leaf-, tree-, and stand-level eco-physiological controls on transpiration. Restricted water use is also additive to other ecosystem services provided by mangroves, such as high carbon sequestration, coastal protection and support of biodiversity within estuarine and marine environments. Low freshwater demand enables mangroves to sustain ecological values of connected estuarine ecosystems with future reductions in freshwater while not competing with the freshwater needs of humans. Conservative water use may also be a characteristic of other emergent blue carbon wetlands.

Understanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude, and spatial distribution of the key environmental and socioeconomic factors driving vegetation change in a southern African savanna. This research was conducted across the Kwando, Okavango and Zambezi catchments of southern Africa (Angola, Namibia, Botswana and Zambia) and explored vegetation cover change across the region from 2001-2010. A novel coupled analysis was applied to model the dynamic biophysical factors then to determine the discrete / social drivers of spatial heterogeneity on vegetation. Previous research applied Dynamic Factor Analysis (DFA), a multivariate times series dimension reduction technique, to ten years of monthly remotely sensed vegetation data (MODIS-derived normalized difference vegetation index, NDVI), and a suite of time-series (monthly) environmental covariates: precipitation, mean, minimum and maximum air temperature, soil moisture, relative humidity, fire and potential evapotranspiration. This initial research was performed at a regional scale to develop meso-scale models explaining mean regional NDVI patterns. The regional DFA predictions were compared to the fine-scale MODIS time series using Kendall's Tau and Sen's Slope to identify pixels where the DFA model we had developed, under or over predicted NDVI. Once identified, a Random Forest (RF) analysis using a series of static social and physical variables was applied to explain these remaining areas of under- and over- prediction to fully explore the drivers of heterogeneity in this savanna system. The RF analysis revealed the importance of protected areas, elevation, soil type, locations of higher population, roads, and settlements, in explaining fine scale differences in vegetation biomass. While the previously applied DFA generated a model of environmental variables driving NDVI, the RF work developed here highlighted human influences dominating that signal. The combined DFRFA model approach explains almost 90% of the variance in NDVI across this landscape from 2001-2010. Our methodology presents a unique coupling of dynamic and static factor analyses, yielding novel insights into savanna heterogeneity, and providing a tool of great potential for researchers and managers alike.

Southern African savannas are an important dryland ecosystem, as they account for up to 54% of the landscape, support a rich variety of biodiversity, and are areas of key landscape change. This paper aims to address the challenges of studying this highly gradient landscape with a grass–shrub–tree continuum. This study takes place in South Luangwa National Park (SLNP) in eastern Zambia. Discretely classifying land cover in savannas is notoriously difficult because vegetation species and structural groups may be very similar, giving off nearly indistinguishable spectral signatures. A support vector machine classification was tested and it produced an accuracy of only 34.48%. Therefore, we took a novel continuous approach in evaluating this change by coupling in situ data with Landsat-level normalized difference vegetation index data (NDVI, as a proxy for vegetation abundance) and blackbody surface temperature (BBST) data into a rule-based classification for November 2015 (wet season) that was 79.31% accurate. The resultant rule-based classification was used to extract mean Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI values by season over time from 2000 to 2016. This showed a distinct separation between each of the classes consistently over time, with woodland having the highest NDVI, followed by shrubland and then grassland, but an overall decrease in NDVI over time in all three classes. These changes may be due to a combination of precipitation, herbivory, fire, and humans. This study highlights the usefulness of a continuous time-series-based approach, which specifically integrates surface temperature and vegetation abundance-based NDVI data into a study of land cover and vegetation health for savanna landscapes, which will be useful for park managers and conservationists globally.

Across savanna landscapes of southern Africa, people are strongly tied to the environment, meaning alterations to the landscape would impact livelihoods and socioecological development. Given the human–environment connection, it is essential to further our understanding of the drivers of savanna vegetation dynamics, and under increasing climate variability, to better understand the vegetation–climate relationship. Monthly time series of Advanced Very High-Resolution Radiometer (AVHRR)- and Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation indices, available from as early as the 1980s, holds promise for the large-scale quantification of complex vegetation–climate dynamics and regional analyses of landscape change as related to global environmental changes. In this work, we employ time series based analyses to examine landscape-level vegetation greening patterns over time and across a significant precipitation gradient. In this study, we show that climate induced reductions in Normalized Difference Vegetation Index (NDVI; i.e., degradation or biomass decline) have had large spatial and temporal impacts across the Kwando, Okavango, and Zambezi catchments of southern Africa. We conclude that over time there have been alterations in the available soil moisture resulting from increases in temperature in every season. Such changes in the ecosystem dynamics of all three basins has led to system-wide changes in landscape greening patterns.

Ventilator sharing is one option to emergently increase ventilator capacity during a crisis but has been criticized for its inability to adjust for individual patient needs. Newer ventilator sharing designs use valves and restrictors to control pressures for each patient. A key component of these designs is an inline Positive End Expiratory Pressure (PEEP) Valve but these are not readily available. Creating an inline PEEP valve by converting a standard bag-valve-mask PEEP valve is possible with the addition of a 3D printed collar. This was a feasibility study assessing the performance and safety of a method for converting a standard PEEP valve into an inline PEEP valve. A collar was designed and printed that covers the exhaust ports of the valve and returns exhaled gases to the ventilator. The collar piece was simple to print and easily assembled with the standard PEEP valve. In bench testing it successfully created differential pressures in 2 simulated expiratory limbs without leaking to the atmosphere at pressures greater than 60 cm of H2O. Our novel inline PEEP valve design shows promise as an option for building a safer ventilator sharing system.