Finding effective ways to monitor laying hen welfare is challenging as UK flock sizes can reach 16,000 birds. Eggs provide potential for welfare monitoring, as they are a daily output with previous evidence of links to stress. We explored the associations between stressors and eggs using two complementary studies. In Study 1, hens experienced social or heat stressors and eggs were scored daily for defects in shell characteristics. All eggs were scored on a three-point scale: 1 (no defect); 2 (minor defects); or 3 (unsuitable for whole egg sale in the UK). Texture defects were higher after stress treatments and were explored further as a promising proxy measure of welfare. In Study 2, eggshell texture from five commercial flocks was scored before versus at the onset of an avian influenza-enforced indoor housing, and scores were correlated with industry data for egg quality. Eggs were more likely to have texture defects after the enforced indoor housing, and manually scored texture correlated significantly with shell strength and shell colour during automated grading. Shell strength was weaker immediately after the enforced indoor housing and eggs were darker. We suggest that eggshell texture could be a useful addition to assessing changes or stresses in a hen’s environment for both research and commercial purposes, but further validation is needed to understand the generalisability of these results to other stressors. Additionally, data already collected in factories, such as shell strength and colour, may provide information on stress and could be valorised for understanding hen welfare.

We developed automated ablation stakes to measure colocated in situ changes in relative glacier-surface elevation and climatological drivers of ablation. The designs, refined over 10 years of development and deployments, implement open-source hardware and common building materials. The ablation stakes record distance to the snow/ice surface, air temperature and relative humidity every 1–15 min. Using these high-frequency data, we demonstrate that melt factors calculated using standard melt-rate vs temperature regressions converge over averaging windows of approximately 12 h or greater. Furthermore, we evaluate an integral approach to estimating temperature-index melt factors for ablation. In a test case on Glaciar Perito Moreno, Argentina, this integral approach reveals an overall positive-degree-day melt factor of 7.5 mm w.e. $^\circ$C−1 d−1. We describe four deployments with iteratively improved designs and provide a list of materials required to construct an automated ablation stake.

By appealing to public concern over environmental issues, Green parties have emerged to gain secure positions in several party systems. However, in Canada, we know very little about why people support the Green Party. This research note draws upon the Canadian Election Study (CES) to explore the ways in which demographic factors, personality traits and individual environmentalism impact vote choice. Theorizing Green Party support as a form of pro-environmental behaviour, we build a model that tests the impact of demographic factors and personality traits as mediated through environmental attitudes. It finds that, while pro-environmental policy attitudes are the strongest predictor of Green Party support, several demographic factors and personality traits—specifically conscientiousness, openness to experience, agreeableness and extraversion—have an effect.

The time-evolution of glacier basal motion remains poorly constrained, despite its importance in understanding the response of glaciers to climate warming. Athabasca Glacier provides an ideal site for observing changes in basal motion over long timescales. Studies from the 1960s provide an in situ baseline dataset constraining ice deformation and basal motion. We use two complementary numerical flow models to investigate changes along a well-studied transverse profile and throughout a larger study area. A cross-sectional flow model allows us to calculate transverse englacial velocity fields to simulate modern and historical conditions. We subsequently use a 3-D numerical ice flow model, Icepack, to estimate changes in basal friction by inverting known surface velocities. Our results reproduce observed velocities well using standard values for flow parameters. They show that basal motion declined significantly (30–40%) and this constitutes the majority (50–80%) of the observed decrease in surface velocities. At the same time, basal resistive stress has remained nearly constant and now balances a much larger fraction of the driving stress. The decline in basal motion over multiple decades of climate warming could serve as a stabilizing feedback mechanism, slowing ice transport to lower elevations, and therefore moderating future mass loss rates.

Background: Patients undergoing hemodialysis are at high risk for healthcare-associated infections; they are at 100 times the risk of Staphylococcus aureus bloodstream infections (BSI) compared with U.S. adults not on hemodialysis. Prior studies found that nasal decolonization with mupirocin prevented S. aureus BSI among hemodialysis patients. We implemented a nasal decolonization intervention in which patients self-administered povidone-iodine (PVI) at each dialysis session. We aimed to assess: 1) hemodialysis patients’ knowledge of their infection risk and their willingness to take an active role in infection prevention; 2) the acceptability of the PVI nasal decolonization intervention. Methods: We performed a stepped wedge cluster randomized trial at 16 outpatient hemodialysis centers. Patients were surveyed: before starting PVI, 1 month after their center started using PVI, and ~6 months after starting PVI. We used a chi-square test to compare results. Results: 469 patients completed at least 1 survey: 400 pre-intervention, 237 at 1 month and 201 at 6 months. Overall, 56% of patients thought that their risk of infection was average or below average compared with an average person in the U.S. (Figure). Over 98% agreed with the statement “One of the most important things I can do for my health is to take an active role in my health care." In the pre-intervention survey, 73% were willing to do “a lot of effort” to prevent an infection. This proportion was similar (73%) in the 2nd survey, but decreased to 63% in the final survey (p < 0 .01). Among 106 patients who reported starting PVI, 85% reported that PVI felt neutral or pleasant, 9.4% reported a side effect, and 79% reported using it during the past 3 dialysis sessions. Among 102 patients who reported using PVI at 6 months, 87% said it felt neutral/pleasant, 3.9% reported a side effect and 75% reported using it during the past 3 dialysis sessions. Side effects included nasal dripping, congestion or burning/stinging, unpleasant smell, headache, yellow tears, and minor nose bleeding. Conclusions: Hemodialysis patients are not aware of their high risk of infection. Although many were willing to expend a lot of effort to prevent an infection, this willingness decreased during an infection prevention intervention. There were few PVI side effects and most patients stated that PVI felt neutral/pleasant, yet many patients chose to not use PVI. Future research should aim to improve patient education on their risk of infection and assess barriers to adherence with infection prevention interventions.

Disclosure: Marin Schweizer: Speaker- 3M; Contracted research-3M; Anitha Vijayan: Honoraria - Quanta, Baxter, Fresenius Consulting, Astute, NxStage

Cognitive behavioural therapy (CBT) is an effective treatment for depression, but a significant minority of clients are difficult to treat, including those with histories of relational trauma. The model of Beck et al. (1979) proposes that adverse childhood experiences lead to negative core beliefs, and these create a susceptibility to depression. However, Beck’s model does not identify trauma as a subset of adverse experiences. An alternative view is that traumatised clients internalise conflicting representations of self and it is conflict, interacting with trauma memories, that creates a vulnerability for depression. In this formulation, methods from the treatment of post-traumatic stress disorder (PTSD) could be incorporated into the treatment of depression, to emotionally process trauma memories and resolve self-identity conflicts. The aims of this study were to: (1) report the treatment of a 67-year-old man with recurrent depression and a history of prolonged relational trauma, and (2) to explore how memory processing from the treatment of PTSD can be incorporated into the treatment of recurrent depression. A single case observational design was used in the long-term treatment of a depressed traumatised client. The client received 47 individual sessions over 19 months in routine clinical practice in a tertiary CBT service. He completed repeated measures of mood, memory intrusions and sleep disruption. The client responded well to treatment with clinically significant improvements across measures of mood, memory and sleep. The effects were sustained over an 18-month follow-up. Memory processing was successfully integrated into a high-intensity treatment for recurrent depression. This is a promising approach for depressed clients with histories of relational trauma.

Why are some constitutions amended more frequently than others? The literature provides few clear answers, as some scholars focus on institutional factors, whereas others emphasize amendment culture. We bridge this divide with new theoretical and empirical insights. Using data from democratic constitutions worldwide and U.S. state constitutions, we examine how social capital reduces the transaction costs imposed by amendment rules. The results indicate that constitutional rigidity decreases amendment frequency, but group membership, civic activism, and political trust can offset the effect of amendment rules. Our findings have important implications for scholars in public law, constitutional and democratic theory, and social movements.

Observational studies suggest that 25-hydroxy vitamin D (25(OH)D) concentration is inversely associated with pain. However, findings from intervention trials are inconsistent. We assessed the effect of vitamin D supplementation on pain using data from a large, double-blind, population-based, placebo-controlled trial (the D-Health Trial). 21 315 participants (aged 60–84 years) were randomly assigned to a monthly dose of 60 000 IU vitamin D3 or matching placebo. Pain was measured using the six-item Pain Impact Questionnaire (PIQ-6), administered 1, 2 and 5 years after enrolment. We used regression models (linear for continuous PIQ-6 score and log-binomial for binary categorisations of the score, namely ‘some or more pain impact’ and ‘presence of any bodily pain’) to estimate the effect of vitamin D on pain. We included 20 423 participants who completed ≥1 PIQ-6. In blood samples collected from 3943 randomly selected participants (∼800 per year), the mean (sd) 25(OH)D concentrations were 77 (sd 25) and 115 (sd 30) nmol/l in the placebo and vitamin D groups, respectively. Most (76 %) participants were predicted to have 25(OH)D concentration >50 nmol/l at baseline. The mean PIQ-6 was similar in all surveys (∼50·4). The adjusted mean difference in PIQ-6 score (vitamin D cf placebo) was 0·02 (95 % CI (−0·20, 0·25)). The proportion of participants with some or more pain impact and with the presence of bodily pain was also similar between groups (both prevalence ratios 1·01, 95 % CI (0·99, 1·03)). In conclusion, supplementation with 60 000 IU of vitamin D3/month had negligible effect on bodily pain.

Because decision making is complicated, political elites seek advice when making decisions, and the ways in which they use that advice has systematic features. But, analyses of decision making among elites usually fail to account for advice. We take advantage of unique information about the advice provided to one set of elites to empirically uncover the effect of advice. Specifically, we examine law clerk recommendations on cert to Justice Blackmun. We find that, even after controlling for known determinants of cert and considering sequential decision making, the advice of a trusted advisor matters greatly.

Using a new measure of urbanity for every federal electoral district in Canada from 1896 to the present, this article describes the long-term development of the urban-rural divide in Canadian federal elections. We focus on three questions: (1) when the urban-rural divide has existed in Canada, identifying three main periods—the 1920s, the 1960s and 1993–present—in which the urban-rural cleavage has been especially important in federal elections; (2) where the urban-rural divide has existed, finding that in the postwar period the urban-rural cleavage is a pan-Canadian phenomenon; and (3) how well urbanity predicts district-level election outcomes. We argue that the urban-rural divide is important for understanding election outcomes during several periods of Canadian political development, and never more so than in recent decades. We conclude by discussing the implications of our findings for research on urban-rural cleavages, Canadian electoral politics and Canadian political development.

To enable new research on local ideology and representation in Canada, we construct a latent measure of the policy ideology of 37,500 Canadian Election Study respondents using 56 policy-relevant questions and then use multilevel regression and poststratification to estimate the average ideological position of each of Canada's 338 federal electoral districts and 250 largest municipalities. We use these local ideology estimates to examine ideological representation in Canadian municipal politics. Combining our municipal ideology estimates with elite survey data from more than 900 Canadian municipal politicians, we find evidence of a strong relationship between mass and elite ideology. This relationship is consistent across differing municipal population sizes and institutional structures. We conclude with additional detail on our publicly available individual and aggregate measures and describe their potential uses for future research on ideology and representation in Canadian politics at all levels.

ABSTRACT IMPACT: A machine learning approach using electronic health records can combine descriptive, population-level factors of pressure injury outcomes. OBJECTIVES/GOALS: Pressure injuries cause 60,000 deaths and cost $26 billion annually in the US, but prevention is laborious. We used clinical data to develop a machine learning algorithm for predicting pressure injury risk and prescribe the timing of intervention to help clinicians balance competing priorities. METHODS/STUDY POPULATION: We obtained 94,745 electronic health records with 7,000 predictors to calibrate a predictive algorithm of pressure injury risk. Machine learning was used to mine features predicting changes in pressure injury risk; random forests outperformed neural networks, boosting and bagging in feature selection. These features were fit to multilevel ordered logistic regression to create an algorithm that generated empirical Bayes estimates informing a decision-rule for follow-up based on individual risk trajectories over time. We used cross-validation to verify predictive validity, and constrained optimization to select a best-fit algorithm that reduced the time required to trigger patient follow-up. RESULTS/ANTICIPATED RESULTS: The algorithm significantly improved prediction of pressure injury risk (p<0.001) with an area under the ROC curve of 0.60 compared to the Braden Scale, a traditional clinician instrument of pressure injury risk. At a specificity of 0.50, the model achieved a sensitivity of 0.63 within 2.5 patient-days. Machine learning identified categorical increases in risk when patients were prescribed vasopressors (OR=16.4, p<0.001), beta-blockers (OR=4.8, p<0.001), erythropoietin stimulating agents (OR=3.0, p<0.001), or were ordered a urinalysis screen (OR=9.1, p<0.001), lipid panel (OR=5.7, p<0.001) or pre-albumin panel (OR=2.0, p<0.001). DISCUSSION/SIGNIFICANCE OF FINDINGS: This algorithm could help hospitals conserve resources within a critical period of patient vulnerability for pressure injury not reimbursed by Medicare. Savings generated by this approach could justify investment in machine learning to develop electronic warning systems for many iatrogenic injuries.

Municipal governments are experts in social non-distancing. From swimming pools to libraries, streetcars to public parks, municipalities bring residents together and move them around—services vital to a vibrant community in ordinary times, but potentially disastrous in a pandemic. Municipal decisions to shutter these services and enforce social distancing are thus crucial for a successful COVID-19 response.

Despite aspirations to be a world-class national curriculum, the Australian Curriculum (AC) has been criticised as ‘manifestly deficient’ (Australian Government Department of Education and Training, 2014 p. 5) as an inclusive curriculum, failing to meet the needs of all students with disabilities (SWD) and their teachers. There is a need for research into the daily attempts of educators to navigate the tension between a ‘top-down’ system-wide curriculum and a ‘bottom-up’ regard for individual student needs, with a view to informing both policy and practice. This article is the first of two research papers in which we report the findings from a national online Research in Special Education (RISE) Australian Curriculum Survey of special educators in special schools, classes, and units regarding their experience using the AC to plan for and teach SWD. Survey results indicated (a) inconsistent use of the AC as the primary basis for developing learning objectives and designing learning experiences, (b) infrequent use of the achievement standards to support assessment and reporting, and (c) considerable supplementation of the AC from other resources when educating SWD. Overall, participants expressed a lack of confidence in translating the AC framework into a meaningful curriculum for SWD. Implications for policy, practice, and future research are discussed.

Rapid, non-destructive methods for measuring seed germination and vigour are valuable. Standard germination and seed vigour were determined using 81 soybean seed lots. From these data, seed lots were separated into high and low germinating seed lots as well as high, medium and low vigour seed lots. Near-infrared spectra (950–1650 nm) were collected for training and validation samples for each seed category and used to create partial least squares (PLS) prediction models. For both germination and vigour, qualitative models provided better discrimination of high and low performing seed lots compared with quantitative models. The qualitative germination prediction models correctly identified low and high germination seed lots with an accuracy between 85.7 and 89.7%. For seed vigour, qualitative predictions for the 3-category (low, medium and high vigour) models could not adequately separate high and medium vigour seeds. However, the 2-category (low, medium plus high vigour) prediction models could correctly identify low vigour seed lots between 80 and 100% and the medium plus high vigour seed lots between 96.3 and 96.6%. To our knowledge, the current study is the first to provide near-infrared spectroscopy (NIRS)-based predictive models using agronomically meaningful cut-offs for standard germination and vigour on a commercial scale using over 80 seed lots.