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This article exposes how disparity in the immigration rules of different visas combines with poor enforcement of labour standards to produce a segmented labour market in the Australian horticulture industry. We argue that the precarious work norms of the horticulture industry result in a ‘demand’ on the part of employers for harvest workers to perform precarious jobs. Such demand has been met by the workers supplied through different segments of temporary migrant labour who may be a particularly attractive form of precarious labour because of the conditionalities they experience as a result of their visa class. Our analysis demonstrates that not only do growers make preferences between local and temporary migrant workers, but they also make preferences between different types of temporary migrant workers. In identifying segmentation between temporary migrant workers on different visa categories, the article makes a significant contribution to the labour market segmentation literature, which hitherto has focused on segmentation between migrant workers and non-migrant workers.
Many countries use employer-sponsored visas to regulate migrant worker recruitment. By tying each sponsored migrant to a single employer, employer-sponsored visas have contributed to problems of workers being underpaid and mistreated. Through a critical assessment of temporary visas in Australia, particularly the Temporary Skill Shortage visa, and an analysis of relevant Australian and international literature, we argue that employer-sponsored visas are fundamentally flawed in their design and should be replaced. We consider various alternative options to employer sponsorship for regulating migrant worker recruitment before proposing the creation of a ‘mobility visa’, which would allow migrant workers to move freely between employers. We argue a mobility visa is a superior model for protecting worker equity and voice while also helping to address labour market needs.
There is some initial evidence that attachment security priming may be useful for promoting engagement in therapy and improving clinical outcomes.
Aims:
This study sought to assess whether outcomes for behavioural activation delivered in routine care could be enhanced via the addition of attachment security priming.
Method:
This was a pragmatic two-arm feasibility and pilot additive randomised control trial. Participants were recruited with depression deemed suitable for a behavioural activation intervention at Step 2 of a Talking Therapies for Anxiety and Depression service. Ten psychological wellbeing practitioners were trained in implementing attachment security priming. Study participants were randomised to either behavioural activation (BA) or BA plus an attachment prime. The diagrammatic prime was integrated into the depression workbook. Feasibility outcomes were training satisfaction, recruitment, willingness to participate and study attrition rates. Pilot outcomes were comparisons of clinical outcomes, attendance, drop-out and stepping-up rates.
Results:
All practitioners recruited to the study, and training satisfaction was high. Of the 39 patients that were assessed for eligibility, 24 were randomised (61.53%) and there were no study drop-outs. No significant differences were found between the arms with regards to drop-out, attendance, stepping-up or clinical outcomes.
Conclusions:
Further controlled research regarding the utility of attachment security priming is warranted in larger studies that utilise manipulation checks and monitor intervention adherence.
Across medicine, key scientific advances in the past couple of decades have deepened knowledge of fundamental mechanisms of disease, leading to new treatments and the possibility of increased personalisation of care. Some of the most important developments in fields as diverse as immunology, the microbiome, genetics, stem cells and artificial intelligence are summarised in this article to raise awareness among psychiatrists and to help understand the opportunities and challenges they may present for mental healthcare in the future.
The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.
Over the last 20 years disasters have increasingly involved children, and pediatric disaster medicine research is growing. However, this research is largely reactive, has not been categorized in terms of the disaster cycle, and the quality of the research is variable. To understand the gaps in current literature and highlight areas for future research, we conducted a scoping review of pediatric disaster medicine literature. This work will help create recommendations for future pediatric disaster medicine research.
Method:
Using a published framework for scoping reviews, we worked with a medical librarian and a multi-institutional team to define the research question, develop eligibility criteria, and to identify a search strategy. We conducted a comprehensive Medline search from 2001-2022, which was distributed to nine reviewers. Each article was independently screened for inclusion by two reviewers. Discrepancies were resolved by a third reviewer.
Inclusion criteria included articles published in English, related to all stages of the disaster cycle, and disaster education, focused on or included pediatric populations; published in academic, peer-reviewed journals, and policies from professional societies.
Results:
967 pediatric disaster medicine articles were imported for screening and 35 duplicates were removed. 932 articles were screened for relevance and 109 were excluded. In 2000, three articles met inclusion criteria and 66 in 2021. We noticed reactive spikes in the number of articles after major disasters. Most articles focused on preparedness and response, with only a few articles on recovery, mitigation, and prevention. Methodology used for most studies was either qualitative or retrospective. Most were single site studies and there were < 10 meta-analyses over the 20 years.
Conclusion:
This scoping review describes the trends in and quality of existing pediatric disaster medicine literature. By identifying the gaps in this body of literature, we can better prioritize future research.
This article presents an historical and comparative analysis of the bargaining power and agency conferred upon migrant workers in Australia under distinct policy regimes. Through an assessment of four criteria – residency status, mobility, skill thresholds and institutional protections – we find that migrant workers arriving in Australia in the period from 1973 to 1996 had high levels of bargaining power and agency. Since 1996, migrant workers’ power and agency has been incrementally curtailed, to the extent that Australia’s labour immigration policy resembles a guest-worker regime where migrants’ rights are restricted, their capacity to bargain for decent working conditions with their employers is truncated and their agency to pursue opportunities available to citizens and permanent residents is diminished. In contrast to recent assessments that Australia’s temporary visa system is working effectively, our analysis indicates that it is failing to protect temporary migrants at work.
This discussion paper by a group of scholars across the fields of health, economics and labour relations argues that COVID-19 is an unprecedented humanitarian crisis from which there can be no return to the ‘old normal’. The pandemic’s disastrous worldwide health impacts have been exacerbated by, and have compounded, the unsustainability of economic globalisation based on the neoliberal dismantling of state capabilities in favour of markets. Flow-on economic impacts have simultaneously created major supply and demand disruptions, and highlighted the growing within-country inequalities and precarity generated by neoliberal regimes of labour market regulation. Taking an Australian and international perspective, we examine these economic and labour market impacts, paying particular attention to differential impacts on First Nations people, developing countries, women, immigrants and young people. Evaluating policy responses in a political climate of national and international leadership very different from those in which major twentieth century crises were addressed, we argue the need for a national and international conversation to develop a new pathway out of crisis.
Wage theft has emerged as a major problem for regulation of work in Australia. Yet, the state has done little to address the issue. In this context, this article considers why there has been recent growth in reported cases of underpayment of wages, particularly of temporary migrant workers, and why the state has failed to implement a strategy to adequately address this problem. The article examines the fragmented nature of employment regulation and visa categories constraining worker agency which, combined with widening avenues for temporary migration, have contributed to the underpayment problem. We also consider how conflicting imperatives of the state, business influence over the policy process and weak political incentives to address underpayment help to account for the state’s inaction.
Looked-after children are at risk of suboptimal attachment patterns and reactive attachment disorder (RAD). However, access to interventions varies widely, and there are no evidence-based interventions for RAD.
Aims
To modify an existing parenting intervention for children with RAD in the UK foster care setting, and test the feasibility of conducting a randomised controlled trial (RCT) of the modified intervention.
Method
The intervention was modified with expert input and tested on a case series. A feasibility and pilot RCT compared the new intervention with usual care. Foster carers and children in their care aged ≤6 years were recruited across nine local authorities, with 1:1 allocation and blind post-treatment assessments. The modified intervention was delivered in-home by trained mental health professionals over 4–6 months. Children were assessed for RAD symptoms, attachment quality and emotional/behavioural difficulties, and foster carers were assessed for sensitivity and stress.
Results
Minimal changes to the intervention programme were necessary, and focused on improving its suitability for the UK foster care context. Recruitment was challenging, and remained below target despite modifications to the protocol and the inclusion of additional sites. Thirty families were recruited to the RCT; 15 were allocated to each group. Most other feasibility outcomes were favourable, particularly high numbers of data and treatment completeness. The revised intervention was positively received by practitioners and foster carers.
Conclusions
A large-scale trial may be feasible, but only if recruitment barriers can be overcome. Dedicated resources to support recruitment within local authorities and wider inclusion criteria are recommended.
The opioid epidemic in the United States is getting worse: in 2020 opioid overdose deaths hit an all-time high of 92,183. This underscored the need for more effective and readily available treatments for patients with opioid use disorder (OUD). Prescription digital therapeutics (PDTs) are FDA-authorized treatments delivered via mobile devices (eg, smartphones). A real-world pilot study was conducted in an outpatient addiction treatment program to evaluate patient engagement and use of a PDT for patients with OUD. The objective was to assess the ability of the PDT to improve engagement and care for patients receiving buprenorphine medication for opioid use disorder (MOUD).
Methods
Patients with OUD treated at an ambulatory addiction treatment clinic were invited to participate in the pilot. The reSET-O PDT is comprised of 31 core therapy lessons plus 36 supplementary lessons, plus contingency management rewards. Patients were asked to complete at least 4 lessons per week, for 12-weeks. Engagement and use data were collected via the PDT and rates of emergency room data were obtained from patient medical records. Data were compared to a similar group of 158 OUD patients treated at the same clinic who did not use the PDT. Abstinence data were obtained from deidentified medical records.
Results
Pilot participants (N = 40) completed a median of 24 lessons: 73.2% completed at least 8 lessons and 42.5% completed all 31 core lessons. Pilot participants had significantly higher rates of abstinence from opioids in the 30 days prior to discharge from the program than the comparison group: 77.5% vs 51.9% (P < .01). Clinician-reported treatment retention for pilot participants vs the comparison group was 100% vs 70.9% 30 days after treatment initiation (P < .01), 87.5% vs 55.1% at 90 days post-initiation (P < .01), and 45.0% vs 38.6% at 180 days post-initiation (P = .46). Emergency room visits within 90 days of discharge from the addiction program were significantly reduced in pilot participants compared to the comparison group (17.3% vs 31.7%, P < .01).
Conclusions
These results demonstrate substantial engagement with a PDT in a real-world population of patients with OUD being treated with buprenorphine. Abstinence and retention outcomes were high compared to patients not using the PDT. These results demonstrate the potential value of PDTs to improve outcomes among patients with OUD, a population for which a significant need for improved treatments exists.
Funding
Trinity Health Innovation and Pear Therapeutics Inc.
This chapter describes methods based on gradient information that achieve faster rates than basic algorithms such as those described in Chapter 3. These accelerated gradient methods, most notably the heavy-ball method and Nesterov’s optimal method, use the concept of momentum which means that each step combines information from recent gradient values but also earlier steps. These methods are described and analyzed using an analysis based on Lyapunov functions. The cases of convex and strongly convex functions are analyzed separately. We motivate these methods using continuous-time limits, which link gradient methods to dynamical systems described by differential equations. We mention also the conjugate gradient method, which was developed separately from the other method but which also makes use of momentum. Finally, we discuss the concept of lower bounds on algorithmic complexity, introducing a function on which no method based on gradients can attain convergence faster than a certain given rate.
Here, we describe methods for minimizing a smooth function over a closed convex set, using gradient information. We first state results that characterize optimality of points in a way that can be checked, and describe the vital operation of projection onto the feasible set. We next describe the projected gradient algorithm, which is in a sense the extension of the steepest-descent method to the constrained case, analyze its convergence, and describe several extensions. We next analyze the conditional-gradient method (also known as “Frank-Wolfe”) for the case in which the feasible set is compact and demonstrate sublinear convergence of this approach when the objective function is convex.
Here, we discuss concepts of duality for convex optimization problems, and algorithms that make use of these concepts. We define the Lagrangian function and its augmented Lagrangian counterpart. We use the Lagrangian to derive optimality conditions for constrained optimization problems in which the constraints are expressed as linear algebraic conditions. We introduce the dual problem, and discuss the concepts of weak and strong duality, and show the existence of positive duality gaps in certain settings. Next, we discuss the dual subgradient method, the augmented Lagrangian method, and the alternating direction method of multipliers (ADMM), which are useful for several types of data science problems.
In this introductory chapter, we outline the ways in which various problems in data analysis can be formulated as optimization problems. Specifically, we discuss least squares problems, problems in matrix optimization (particularly those involving low-rank matrices), linear and kernel support vector machines, binary and multiclass logistic regression, and deep learning. We also outline the scope of the remainder of the book.
We describe the stochastic gradient method, the fundamental algorithm for several important problems in data science, including deep learning. We give several example problems for which this method is suitable, then described its operation for the simple problem of computing a mean of a collection of values. We related it to a classical method, the Kaczmarz method for solving a system of linear equalities and inequalities. Next, we describe the key assumptions to be used in convergence analysis, then describe the convergence rates attainable by several variants of stochastic gradient under several scenarios. Finally, we discuss several aspects of practical implementation of stochastic gradient, including minibatching and acceleration.
We outline theoretical foundations for smooth optimization problems. First, we define the different types of minimizers (solutions) of unconstrained optimization problems. Next, we state Taylor’s theorem, the fundamental theorem of smooth optimization, which allows us to approximate general smooth functions by simpler (linear or quadratic) functions based on information at the current point. We show how minima can be characterized by optimality conditions involving the gradient or Hessian, which can be checked in practice. Finally, we define the convexity of sets and functions, an important property that arises often in practice and that can be exploited by the algorithms described in the remainder of the book.