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The Road Safety Remuneration Act 2012 (Cth) (the Act) explicitly enables the Road Safety Remuneration Tribunal to make orders that can impose binding requirements on all the participants in the road transport supply chain, including consignors and consignees at the apex of the chain, for the pay and safety of both employee and independent contractor drivers. The tribunal is also specifically empowered to make enforceable orders to reduce or remove remuneration related incentives and pressures that contribute to unsafe work practices in the road transport industry. Recently the tribunal handed down its first order. The article considers whether, and the degree to which, the tribunal has been willing to exercise its explicit power to impose enforceable obligations on consignors and consignees – such as large supermarket chains – at the apex of road transport supply chains. It examines the substance and extent of the obligations imposed by the tribunal, including whether the tribunal has exercised the full range of powers vested in it by the Act. We contend that the tribunal's first order primarily imposes obligations on direct work providers and drivers without making large, powerful consignors and consignees substantively responsible for driver pay and safety. We argue that the tribunal's first order could have more comprehensively fulfilled the objectives of the Act by more directly addressing the root causes of low pay and poor safety in the road transport industry.
Outlining the economic significance of the role of global supply chains (GSCs) in the organisation of the global economy, this paper initially presents some indications of health and safety outcomes in low- and middle-income counties (LMICs) where GSCs source much of the production destined for use in advanced economies. It goes on to discuss the operational dynamics of these chains and the corporate priorities that they reflect, which, it argues, do little to improve the poor work health and safety (WHS) outcomes in LMICs. It then examines evidence for the effectiveness of various private and public regulatory strategies that are claimed to bring about improved health and safety practices and outcomes among GSC suppliers in these countries. The paper critically evaluates this evidence and argues that, while there may be some examples of effective strategies and regulatory practices in particular contexts, their overall influence remains limited. It identifies and discusses the principal reasons for these limitations and concludes that the global regulation of conditions of labour – including WHS – at the end of GSCs falls well short of universal best practice and is, more generally, insufficient to counter the economic forces working against the maintenance of adequate standards of worker protection.
This article brings three decades of broadly consistent survey data on survey respondents’ feelings about the parties as evidence of affective polarization. It also presents evidence about policy differences among the parties and makes an explicit link between elite and mass data with multilevel modelling. The article shows that affective polarization is real and also demonstrates its connection to the ideological landscape. But it also shows that conceptual categories originating in the United States must be adapted to Canada's multiparty system and to the continuing contrasts between Quebec and the rest of Canada. It suggests that accounts of Canada's twentieth-century party system may not apply to the twenty-first century.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) hospital outbreaks have been common and devastating during the coronavirus disease 2019 (COVID-19) pandemic. Understanding SARS-CoV-2 transmission in these environments is critical for preventing and managing outbreaks.
Design:
Outbreak investigation through epidemiological mapping and whole-genome sequencing phylogeny.
Setting:
Hospital in-patient medical unit outbreak in Toronto, Canada, from November 2020 to January 2021.
Participants:
The outbreak involved 8 patients and 10 staff and was associated with 3 patient deaths.
Results:
Patients being cared for in geriatric chairs at the nursing station were at high risk for both acquiring and transmitting SARS-CoV-2 to other patients and staff. Furthermore, given the informal nature of these transmissions, they were not initially recognized, which led to further transmission and missing the opportunity for preventative COVID-19 therapies.
Conclusions:
During outbreak prevention and management, the risk of informal patient care settings, such as geriatric chairs, should be considered. During high-risk periods or during outbreaks, efforts should be made to care for patients in their rooms when possible.
We performed an epidemiological investigation and genome sequencing of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) to define the source and scope of an outbreak in a cluster of hospitalized patients. Lack of appropriate respiratory hygiene led to SARS-CoV-2 transmission to patients and healthcare workers during a single hemodialysis session, highlighting the importance of infection prevention precautions.
Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet is inversely associated with type 2 diabetes mellitus (T2DM) risk. Metabolic changes due to DASH adherence and their potential relationship with incident T2DM have not been described. The objective is to determine metabolite clusters associated with adherence to a DASH-like diet in the Insulin Resistance Atherosclerosis Study cohort and explore if the clusters predicted 5-year incidence of T2DM. The current study included 570 non-diabetic multi-ethnic participants aged 40–69 years. Adherence to a DASH-like diet was determined a priori through an eighty-point scale for absolute intakes of the eight DASH food groups. Quantitative measurements of eighty-seven metabolites (acylcarnitines, amino acids, bile acids, sterols and fatty acids) were obtained at baseline. Metabolite clusters related to DASH adherence were determined through partial least squares (PLS) analysis using R. Multivariable-adjusted logistic regression was used to explore the associations between metabolite clusters and incident T2DM. A group of acylcarnitines and fatty acids loaded strongly on the two components retained under PLS. Among strongly loading metabolites, a select group of acylcarnitines had over 50 % of their individual variance explained by the PLS model. Component 2 was inversely associated with incident T2DM (OR: 0·89; (95 % CI 0·80, 0·99), P-value = 0·043) after adjustment for demographic and metabolic covariates. Component 1 was not associated with T2DM risk (OR: 1·02; (95 % CI 0·88, 1·19), P-value = 0·74). Adherence to a DASH-type diet may contribute to reduced T2DM risk in part through modulations in acylcarnitine and fatty acid physiology.
Quantitative plant biology is an interdisciplinary field that builds on a long history of biomathematics and biophysics. Today, thanks to high spatiotemporal resolution tools and computational modelling, it sets a new standard in plant science. Acquired data, whether molecular, geometric or mechanical, are quantified, statistically assessed and integrated at multiple scales and across fields. They feed testable predictions that, in turn, guide further experimental tests. Quantitative features such as variability, noise, robustness, delays or feedback loops are included to account for the inner dynamics of plants and their interactions with the environment. Here, we present the main features of this ongoing revolution, through new questions around signalling networks, tissue topology, shape plasticity, biomechanics, bioenergetics, ecology and engineering. In the end, quantitative plant biology allows us to question and better understand our interactions with plants. In turn, this field opens the door to transdisciplinary projects with the society, notably through citizen science.
There is an enduring belief amongst some that segregation is worsening and undermining social cohesion, and that this is especially visible in the growing divides between the schools in which our children are educated. This book uses up-to-date evidence to interrogate some of the controversial claims made by the 2016 Casey Review, providing an analysis of contemporary patterns of ethnic, residential and social segregation, and looking at the ways that these changing geographies interact with each other.
There is evidence that depression can be prevented; however, traditional approaches face significant scalability issues. Digital technologies provide a potential solution, although this has not been adequately tested. The aim of this study was to evaluate the effectiveness of a new smartphone app designed to reduce depression symptoms and subsequent incident depression amongst a large group of Australian workers.
Methods
A randomized controlled trial was conducted with follow-up assessments at 5 weeks and 3 and 12 months post-baseline. Participants were employed Australians reporting no clinically significant depression. The intervention group (N = 1128) was allocated to use HeadGear, a smartphone app which included a 30-day behavioural activation and mindfulness intervention. The attention-control group (N = 1143) used an app which included a 30-day mood monitoring component. The primary outcome was the level of depressive symptomatology (PHQ-9) at 3-month follow-up. Analyses were conducted within an intention-to-treat framework using mixed modelling.
Results
Those assigned to the HeadGear arm had fewer depressive symptoms over the course of the trial compared to those assigned to the control (F3,734.7 = 2.98, p = 0.031). Prevalence of depression over the 12-month period was 8.0% and 3.5% for controls and HeadGear recipients, respectively, with odds of depression caseness amongst the intervention group of 0.43 (p = 0.001, 95% CI 0.26–0.70).
Conclusions
This trial demonstrates that a smartphone app can reduce depression symptoms and potentially prevent incident depression caseness and such interventions may have a role in improving working population mental health. Some caution in interpretation is needed regarding the clinical significance due to small effect size and trial attrition.
Trial Registration Australian and New Zealand Clinical Trials Registry (www.anzctr.org.au/) ACTRN12617000548336
The Murchison Widefield Array (MWA) has observed the entire southern sky (Declination,
$\delta< 30^{\circ}$
) at low radio frequencies, over the range 72–231MHz. These observations constitute the GaLactic and Extragalactic All-sky MWA (GLEAM) Survey, and we use the extragalactic catalogue (EGC) (Galactic latitude,
$|b| >10^{\circ}$
) to define the GLEAM 4-Jy (G4Jy) Sample. This is a complete sample of the ‘brightest’ radio sources (
$S_{\textrm{151\,MHz}}>4\,\text{Jy}$
), the majority of which are active galactic nuclei with powerful radio jets. Crucially, low-frequency observations allow the selection of such sources in an orientation-independent way (i.e. minimising the bias caused by Doppler boosting, inherent in high-frequency surveys). We then use higher-resolution radio images, and information at other wavelengths, to morphologically classify the brightest components in GLEAM. We also conduct cross-checks against the literature and perform internal matching, in order to improve sample completeness (which is estimated to be
$>95.5$
%). This results in a catalogue of 1863 sources, making the G4Jy Sample over 10 times larger than that of the revised Third Cambridge Catalogue of Radio Sources (3CRR;
$S_{\textrm{178\,MHz}}>10.9\,\text{Jy}$
). Of these G4Jy sources, 78 are resolved by the MWA (Phase-I) synthesised beam (
$\sim2$
arcmin at 200MHz), and we label 67% of the sample as ‘single’, 26% as ‘double’, 4% as ‘triple’, and 3% as having ‘complex’ morphology at
$\sim1\,\text{GHz}$
(45 arcsec resolution). We characterise the spectral behaviour of these objects in the radio and find that the median spectral index is
$\alpha=-0.740 \pm 0.012$
between 151 and 843MHz, and
$\alpha=-0.786 \pm 0.006$
between 151MHz and 1400MHz (assuming a power-law description,
$S_{\nu} \propto \nu^{\alpha}$
), compared to
$\alpha=-0.829 \pm 0.006$
within the GLEAM band. Alongside this, our value-added catalogue provides mid-infrared source associations (subject to 6” resolution at 3.4
$\mu$
m) for the radio emission, as identified through visual inspection and thorough checks against the literature. As such, the G4Jy Sample can be used as a reliable training set for cross-identification via machine-learning algorithms. We also estimate the angular size of the sources, based on their associated components at
$\sim1\,\text{GHz}$
, and perform a flux density comparison for 67 G4Jy sources that overlap with 3CRR. Analysis of multi-wavelength data, and spectral curvature between 72MHz and 20GHz, will be presented in subsequent papers, and details for accessing all G4Jy overlays are provided at https://github.com/svw26/G4Jy.
The entire southern sky (Declination,
$\delta< 30^{\circ}$
) has been observed using the Murchison Widefield Array (MWA), which provides radio imaging of
$\sim$
2 arcmin resolution at low frequencies (72–231 MHz). This is the GaLactic and Extragalactic All-sky MWA (GLEAM) Survey, and we have previously used a combination of visual inspection, cross-checks against the literature, and internal matching to identify the ‘brightest’ radio-sources (
$S_{\mathrm{151\,MHz}}>4$
Jy) in the extragalactic catalogue (Galactic latitude,
$|b| >10^{\circ}$
). We refer to these 1 863 sources as the GLEAM 4-Jy (G4Jy) Sample, and use radio images (of
${\leq}45$
arcsec resolution), and multi-wavelength information, to assess their morphology and identify the galaxy that is hosting the radio emission (where appropriate). Details of how to access all of the overlays used for this work are available at https://github.com/svw26/G4Jy. Alongside this we conduct further checks against the literature, which we document here for individual sources. Whilst the vast majority of the G4Jy Sample are active galactic nuclei with powerful radio-jets, we highlight that it also contains a nebula, two nearby, star-forming galaxies, a cluster relic, and a cluster halo. There are also three extended sources for which we are unable to infer the mechanism that gives rise to the low-frequency emission. In the G4Jy catalogue we provide mid-infrared identifications for 86% of the sources, and flag the remainder as: having an uncertain identification (129 sources), having a faint/uncharacterised mid-infrared host (126 sources), or it being inappropriate to specify a host (2 sources). For the subset of 129 sources, there is ambiguity concerning candidate host-galaxies, and this includes four sources (B0424–728, B0703–451, 3C 198, and 3C 403.1) where we question the existing identification.
Most of the focus of this book has been on ethnic segregation, reflecting the discourse found in the media and prominent in government policy documents. However, there is a strong intersectionality between social and ethnic dis-/advantage, which means processes of socio-economic separation are linked to patterns of ethnic segregation in ways that are not easily disentangled. The purpose of this chapter is not to try and do so but, instead, to look for evidence that within ethnic groups, and within a system of constrained school choice, the more or less affluent have different amounts of segregation from other ethnic groups, with this being related to the different types of school they attend. That evidence is found with those of the White British who are not eligible for free school meals (FSMs) generally the most segregated from /least exposed to other ethnic groups, with the effects of academically selective and some religiously selective schools contributing to the differences.
Introduction
To this point, the book has focused on ethnic segregation, with only passing mention of socio-economic segregation and the idea that people of different incomes, affluence or class can reside and be educated separately from one another. Socio-economic and ethnic segregation are intertwined. The spatial inequalities that divide people by wealth and social background sustain and are sustained by inequalities between ethnic groups too. Hence, there is a strong intersectionality between social and ethnic dis-/advantage, as Reni Eddo-Lodge (2017), among others, has eloquently articulated (see Chapter 1). But, despite the overlap, the concepts of ethnic and socio-economic segregation are not exactly reducible to each another. To talk of ethnic segregation is to look more at where people of different ethno-cultural backgrounds are living and attending schools; to talk of socio-economic segregation is to consider the same but for people of different socio-economic positions and/or class.
The attention we have given to ethnic segregation is a reflection of how segregation is conveyed in recent government policy documents. However, it is also the same focus that has been criticized – rightly – for ignoring or, at least, downplaying the socio-economic factors that generate inequalities of opportunity for different ethnic groups and create geographically differentiated outcomes in regard to employment, health, housing, health, the criminal justice system, education and so forth, all of which affect where people live and go to school.
Data about the school-age population in LEAs provide contextual information about what is happening in terms of patterns of ethnic segregation at a broad geographic scale. In regard to the number of each ethnic group per local authority, the White British now have greater potential to be ‘exposed’ to other groups than they did in the past (to reside and be schooled alongside them) because the numbers of those other groups have grown. The reverse is not true, however, because the potential exposure of ‘minority’ groups to the White British has declined with their reduced number or lower growth rate compared to most other groups. All but ten authorities have a more diverse school-age population overall.
Introduction
Chapter 2 looked at the changing numbers of various ethnic groups in the state schools of English LEAs over the period from 2010 to 2017. It found a decreasing percentage of the pupils to be White British, with that decline due to a smaller number of that group in secondary schools (but not primary schools, where their number is rising), and also due to the rising number of pupils from other ethnic groups (with the exception of Black Caribbeans, whose numbers have declined in both primary and secondary schools).
The picture sketched was one of increasing diversity across large parts of the country at the LEA scale, with the school-age population less dominated by the White British, though it remains by far the largest group. Although a decline in the White British is notable in some conurbations, there are many other places where not only are the White British increasing in number, so too are other ethnic groups. In this chapter, we turn to the sorts of formal measures of segregation widely used in academic writing and in policy research. Doing so allows us to both to colour the picture more fully and to introduce the measures ahead of more geographically nuanced analysis from Chapter 4 onwards.
Measuring segregation
Segregation means that when we map where members of different ethnic groups are living or schooled, those maps are not all the same and each shows geographical variation. Put simply, ‘different’ people live in different places.
The English systems of school choice and allocation may result in greater ethnic segregation between schools than between neighbourhoods. This chapter looks at that proposition and asks to what extent school levels of ethnic segregation reflect neighbourhood ethnic composition, where do they not, and for which types of school are the differences greatest? It makes a simple comparison of the segregation between schools and between neighbourhoods then, acknowledging the limitations of that comparison, employs a more sophisticated analysis to compare the diversity of schools’ intakes with what they would look like under a hypothetical system without choice. In the majority of cases, intakes into schools reflect the neighbourhoods that surround them and are not dissimilar to what would be expected under a neighbourhood-based system of pupil allocation. There is little evidence that the current system of school choice raises ethnic segregation substantially.
Introduction
The preceding chapter ended by suggesting patterns of ethnic segregation for schools are linked to those for neighbourhoods. If true, it is not surprising: although the English state school system is described as offering choice, it operates with constraints on that choice – many of those constraints acting geographically. In general, the closest schools are the most accessible and the ones that a pupil has greatest chances of being admitted to. Consequently, school intakes are not independent of the neighbourhoods that surround each school.
Even so, it is not a strictly neighbourhood-based system – pupils (or their parents) have a choice set that, albeit geographically constrained, need not be the same for even those who live in close proximity to one another. That means that it is possible for pupils to separate along ethnic lines. Whether they do or not is the subject of this chapter.
School choice and its potential effects on neighbourhood segregation
In principle, any pupil is free to apply to any school and that school is obliged to admit the pupil if there are sufficient places available (assuming the school does not operate other admissions criteria that would limit entry, such as being academically selective or a single-sex school)