26 research outputs found

    Adult-perpetrated Animal Abuse: Development of a Proclivity Scale

    Get PDF
    There is a clear discrepancy in the reporting of animal cruelty complaints, prosecutions and convictions suggesting that any prevalence figures of abuse are significant under-representations. Therefore, it can be inferred that there is a large number of animal abusers who are unapprehended. Currently there is no validated tool that assesses the proclivity or propensity to engage in animal abuse amongst members of the general public. Such a tool would enable researchers to study individuals who may think like animal abusers or may be unapprehended offenders themselves. This paper presents the newly developed Animal Abuse Proclivity Scale (AAPS) and some preliminary findings. The results from our two studies show that: (1) the psychometric properties of the AAPS indicate that the scale is a highly reliable measure; (2) the AAPS relates to measures assessing offence-supportive attitudes and reflects the gender differences seen in the literature; and (3) the AAPS demonstrates cross-national validity. These findings support that the AAPS, similar to other offending proclivity measures, is a tool that can be used to examine the factors most related to animal abuse propensity. We discuss how the AAPS can contribute to future developments in theory and practice in the field

    Dynamics of Charcoal Alteration in a Tropical Biome: A Biochar-Based Study

    Get PDF
    Pyrogenic carbon (PyC) is a polyaromatic residue of the incomplete combustion of biomass or fossil fuels. There is a growing recognition that PyC forms an important part of carbon budgets, due to production rates of 116–385 Tg C yr, and the size and ubiquity of PyC stocks in global carbon reservoirs. At least a proportion of PyC exists in a highly recalcitrant chemical form, raising the prospect of long-term carbon sequestration through soil amendment with “biochar,” which is generally produced with the aim of making a particularly recalcitrant form of PyC. However, there is growing evidence that some PyC, including biochar, can be both physically and chemically altered and degraded upon exposure to the environment over annual timescales, yet there is a lack of information concerning the mechanisms and determining factors of degradation. Here, we investigate three main factors; production temperature, feedstock composition, and the characteristics of the environment to which the material is exposed (e.g., pH, organic matter composition, oxygen availability) by analysis of biochar samples in a litterbag experiment before and after a year-long field study in the tropical rainforests of northeast Australia. We find that non-lignocellulosic feedstock has lower aromaticity, plus lower O/C and H/C ratios for a given temperature, and consequently lower carbon sequestration potential. The rate at which samples are altered is production temperature-dependant; however even in the highest temperature samples loss of the semi-labile aromatic carbon component is observed over 1 year. The results of 13C-MAS-NMR measurements suggest that direct oxygenation of aromatic structures may be even more important than carboxylation in environmental alteration of biochar (as a subset of PyC). There is a clear effect of depositional environment on biochar alteration even after the relatively short timescale of this study, as changes are most extensive in the most oxygenated material that was exposed on the soil surface. This is most likely the result of mineral ingress and colonization by soil microbiota. Consequently, oxygen availability and physical or chemical protection from sunlight and/or rainwater is vital in determining the alteration trajectory of this material

    First 20 years of DNDC: Model evolution and GRAMP.

    Get PDF
    The DNDC (DeNitrification and DeComposition) model was first developed by Li et al. (1992) as a rain event-driven process-orientated simulation model for nitrous oxide, carbon dioxide and nitrogen gas emissions from the agricultural soils in the U.S. Over the last 20 years, the model has been modified and adapted by various research groups around the world to suit specific purposes and circumstances. The Global Research Alliance Modelling Platform (GRAMP) is a UK-led initiative for the establishment of a purposeful and credible web-based platform initially aimed at users of the DNDC model. With the aim of improving the predictions of soil C and N cycling in the context of climate change the objectives of GRAMP are to: 1) to document the existing versions of the DNDC model; 2) to create a family tree of the individual DNDC versions; 3) to provide information on model use and development; and 4) to identify strengths, weaknesses and potential improvements for the model

    The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

    Get PDF
    Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

    COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

    Get PDF
    BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK
    corecore