Socio-structural influences on the work participation of refugees : an exploratory systematic mixed studies review

This systematic mixed studies review aimed at synthesizing evidence from studies related to the influences on the work participation of people with refugee status (PWRS). The review focused on the role of proximal socio-structural barriers on work participation by PWRS while foregrounding related distal, intermediate, proximal, and meta-systemic influences. For the systematic search of the literature, we focused on databases that addressed work, well-being, and social policy in refugee populations, including, Medline, CINAHL, PsycInfo, Web of Science, Scopus, and Sociological Abstracts. Of the studies reviewed, 16 of 39 met the inclusion criteria and were retained for the final analysis. We performed a narrative synthesis of the evidence on barriers to work participation by PWRS, interlinking clusters of barriers potent to their effects on work participation. Findings from the narrative synthesis suggest that proximal factors, those at point of entry to the labor market, influence work participation more directly than distal or intermediate factors. Distal and intermediate factors achieve their effects on work participation by PWRS primarily through meta-systemic interlinkages, including host-country documentation and refugee administration provisions.

Factors affecting two- and five-year re-offence rates in Queensland drink drivers

Risk factors for repeat drink driving, an important road safety issue, are well known, but estimates of Australian recidivism rates by risk factors, apart from a recent NSW study, are not. Driving records of a cohort of Queensland drink drivers matched by age, region, BAC level and prior offence to participants in a drink driving rehabilitation program were used to estimate sex-specific two- and five-year re-offence rates overall and by these factors. Estimates of the proportion of Queensland drink drivers with a prior DD offence in 2004 were used to standardise rates to the Queensland drink driving population. Rates were higher in remote areas, as were rates in males, young drivers, drivers with high BAC levels and in drivers with one and especially with at least two prior DD convictions. Five-year rates for Queensland were estimated as 21.8% in males and 16.4% in females, appreciably higher than in NSW.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Why renal nurses cope so well with their workplace stressors

Background: Previous studies have found significant stressors experienced by nurses working in haemodialysis units yet renal nurses appear to report less burnout than other nurses. Objectives: This study aims to undertake an inductive process to better understand the stressors and the coping strategies used by renal nurses that may lead to resilience. Method: Sixteen haemodialysis nurses from a metropolitan Australian hospital and two satellite units participated in open-ended interviews. Data were analysed from a grounded theory methodology. Measures of burnout and resilience were also obtained. Results: Two major categories of stressors emerged. First, due to prolonged patient contact, family-like relationships developed that lead to the blurring of boundaries. Second, participants experienced discrimination from both patients and staff. Despite these stressors, the majority of participants reported low burnout and moderately high-to-high levels of resilience. The major coping strategy that appeared to promote resilience was emotional distancing, while emotional detachment appeared to promote burn-out. Conclusion: Assisting nurses to use emotional distancing, rather than emotional detachment strategies to engender a sense of personal achievement may promote resilience.

A dynamic wheel-rail impact analysis of railway track under wheel flat by Finite Element Analysis

Wheel–rail interaction is one of the most important research topics in railway engineering. It involves track impact response, track vibration and track safety. Track structure failures caused by wheel–rail impact forces can lead to significant economic loss for track owners through damage to rails and to the sleepers beneath. Wheel–rail impact forces occur because of imperfections in the wheels or rails such as wheel flats, irregular wheel profiles, rail corrugations and differences in the heights of rails connected at a welded joint. A wheel flat can cause a large dynamic impact force as well as a forced vibration with a high frequency, which can cause damage to the track structure. In the present work, a three-dimensional (3-D) finite element (FE) model for the impact analysis induced by the wheel flat is developed by use of the finite element analysis (FEA) software package ANSYS and validated by another validated simulation. The effect of wheel flats on impact forces is thoroughly investigated. It is found that the presence of a wheel flat will significantly increase the dynamic impact force on both rail and sleeper. The impact force will monotonically increase with the size of wheel flats. The relationships between the impact force and the wheel flat size are explored from this finite element analysis and they are important for track engineers to improve their understanding of the design and maintenance of the track system.

School-based programs for increasing connectedness and reducing risk behavior : a systematic review

School connectedness has a significant impact on adolescent outcomes, including reducing risk taking behavior. This paper critically examines the literature on school-based programs targeting increased connectedness for reductions in risk taking. Fourteen articles describing seven different school-based programs were reviewed. Programs drew on a range of theories to increase school connectedness, and evaluations conducted for the majority of programs demonstrated positive changes in school connectedness, risk behavior, or a combination of the two. Many of the reviewed programs involved widespread school system change, however, which is frequently a complex and time consuming task. Future research is needed to examine the extent of intervention complexity required to result in change. This review also showed a lack of consistency in definitions and measurement of connectedness as well as few mediation analyses testing assumptions of impact on risk taking behavior through increases in school connectedness. Additionally, this review revealed very limited evaluation of the elements of multi-component programs that are most effective in increasing school connectedness and reducing adolescent risk taking.

On the commonly accepted assumptions regarding observed motor vehicle crash counts at transport system locations

Readily accepted knowledge regarding crash causation is consistently omitted from efforts to model and subsequently understand motor vehicle crash occurrence and their contributing factors. For instance, distracted and impaired driving accounts for a significant proportion of crash occurrence, yet is rarely modeled explicitly. In addition, spatially allocated influences such as local law enforcement efforts, proximity to bars and schools, and roadside chronic distractions (advertising, pedestrians, etc.) play a role in contributing to crash occurrence and yet are routinely absent from crash models. By and large, these well-established omitted effects are simply assumed to contribute to model error, with predominant focus on modeling the engineering and operational effects of transportation facilities (e.g. AADT, number of lanes, speed limits, width of lanes, etc.) The typical analytical approach—with a variety of statistical enhancements—has been to model crashes that occur at system locations as negative binomial (NB) distributed events that arise from a singular, underlying crash generating process. These models and their statistical kin dominate the literature; however, it is argued in this paper that these models fail to capture the underlying complexity of motor vehicle crash causes, and thus thwart deeper insights regarding crash causation and prevention. This paper first describes hypothetical scenarios that collectively illustrate why current models mislead highway safety researchers and engineers. It is argued that current model shortcomings are significant, and will lead to poor decision-making. Exploiting our current state of knowledge of crash causation, crash counts are postulated to arise from three processes: observed network features, unobserved spatial effects, and ‘apparent’ random influences that reflect largely behavioral influences of drivers. It is argued; furthermore, that these three processes in theory can be modeled separately to gain deeper insight into crash causes, and that the model represents a more realistic depiction of reality than the state of practice NB regression. An admittedly imperfect empirical model that mixes three independent crash occurrence processes is shown to outperform the classical NB model. The questioning of current modeling assumptions and implications of the latent mixture model to current practice are the most important contributions of this paper, with an initial but rather vulnerable attempt to model the latent mixtures as a secondary contribution.

Benefit cost analysis applied to behavioral and engineering safety countermeasures in San Francisco, California

The state of the practice in safety has advanced rapidly in recent years with the emergence of new tools and processes for improving selection of the most cost-effective safety countermeasures. However, many challenges prevent fair and objective comparisons of countermeasures applied across safety disciplines (e.g. engineering, emergency services, and behavioral measures). These countermeasures operate at different spatial scales, are funded often by different financial sources and agencies, and have associated costs and benefits that are difficult to estimate. This research proposes a methodology by which both behavioral and engineering safety investments are considered and compared in a specific local context. The methodology involves a multi-stage process that enables the analyst to select countermeasures that yield high benefits to costs, are targeted for a particular project, and that may involve costs and benefits that accrue over varying spatial and temporal scales. The methodology is illustrated using a case study from the Geary Boulevard Corridor in San Francisco, California. The case study illustrates that: 1) The methodology enables the identification and assessment of a wide range of safety investment types at the project level; 2) The nature of crash histories lend themselves to the selection of both behavioral and engineering investments, requiring cooperation across agencies; and 3) The results of the cost-benefit analysis are highly sensitive to cost and benefit assumptions, and thus listing and justification of all assumptions is required. It is recommended that a sensitivity analyses be conducted when there is large uncertainty surrounding cost and benefit assumptions.

Essential elements for recruitment and retention : Generation Y

Purpose: Generation Y (Gen Y) is the newest and largest generation entering the workforce. Gen Y may differ from previous generations in work-related characteristics which may have recruitment and retention repercussions. Currently, limited theoretically-based research exists regarding Gen Y’s work expectations and goals in relation to undergraduate students and graduates. Design/methodology/approach: This study conducted a theoretically-based investigation of the work expectations and goals of student- and working-Gen Y individuals based within a framework incorporating both expectancy-value and goal setting theories. N = 398 provided useable data via an on-line survey. Findings: Overall, some support was found for predictions with career goals loading on a separate component to daily work expectations and significant differences between student- and working- Gen Y on career goals. No significant differences were found, however, between the two groups in daily work expectations. Research limitations/implications: Future research may benefit from adopting a theoretical framework which assesses both daily work expectations and career goals when examining the factors which motivate Gen Y’s decisions to join and remain at a particular organisation. Practical implications: At a practical level, based on the findings, some examples are provided of the means by which organisations may draw upon daily work expectations and career goals of importance to Gen Y and, in doing so, influence the likelihood that a Gen Y individual will join and remain at their particular organisation. Originality/value: This research has demonstrated the utility of adopting a sound theoretical framework in furthering understanding about the motivations which influence organisations’ ability to recruit and retain Gen Y, among both student Gen Y as well as those Gen Y individuals who are already working.

Factors influencing caregiver : care receiver communication and its impact on the well-being of older care receivers

Examined communication between frail older people and their caregiving spouses (CGSs), and its relation to well-being in older care receivers. 53 community residing spousal dyads completed questionnaires about their well-being, relational satisfaction, and communication patterns. Conversations between the dyads were also videotaped and analyzed. The type of communication used by the CGSs was influenced by their sex, their earlier relationship with their spouse, and their level of well-being. CGSs who used an overly directive communication tone with their spouse were likely to be wives and CGSs who had a high degree of autonomy in their earlier relationship with their spouse. Low levels of life satisfaction and high affect balance in CGSs were associated with CGSs using a more patronizing tone. The well-being of care receivers was also related to their perceptions of their CGSs' communication. Care receivers who perceived their CGSs' communication as patronizing reported low levels of affect balance and high levels of conflict in the relationship. Findings suggest that certain characteristics of CGSs are related to the type of communication they use when conversing with their partner, although the relations are not always as expected.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Social cars : sensing, gathering, sharing and conveying social cues to road users

Intelligent Transport Systems (ITS) resembles the infrastructure for ubiquitous computing in the car. It encompasses a) all kinds of sensing technologies within vehicles as well as road infrastructure, b) wireless communication protocols for the sensed information to be exchanged between vehicles (V2V) and between vehicles and infrastructure (V2I), and c) appropriate intelligent algorithms and computational technologies that process these real-time streams of information. As such, ITS can be considered a game changer. It provides the fundamental basis of new, innovative concepts and applications, similar to the Internet itself. The information sensed or gathered within or around the vehicle has led to a variety of context-aware in-vehicular technologies within the car. A simple example is the Anti-lock Breaking System (ABS), which releases the breaks when sensors detect that the wheels are locked. We refer to this type of context awareness as vehicle/technology awareness. V2V and V2I communication, often summarized as V2X, enables the exchange and sharing of sensed information amongst cars. As a result, the vehicle/technology awareness horizon of each individual car is expanded beyond its observable surrounding, paving the way to technologically enhance such already advanced systems. In this chapter, we draw attention to those application areas of sensing and V2X technologies, where the human (driver), the human’s behavior and hence the psychological perspective plays a more pivotal role. The focal points of our project are illustrated in Figure 1: In all areas, the vehicle first (1) gathers or senses information about the driver. Rather than to limit the use of such information towards vehicle/technology awareness, we see great potential for applications in which this sensed information is then (2) fed back to the driver for an increased self-awareness. In addition, by using V2V technologies, it can also be (3) passed to surrounding drivers for an increased social awareness, or (4), pushed even further, into the cloud, where it is collected and visualized for an increased, collective urban awareness within the urban community at large, which includes all city dwellers.

The use of CCTV to police public spaces : a case of big brother or big friend?

This paper considers the role of CCTV (closed circuit television) in the surveillance, policing and control of public space in urban and rural locations, specifically in relation to the use of public space by young people. The use of CCTV technology in public spaces is now an established and largely uncontested feature of everyday life in a number of countries and the assertion that they are essentially there for the protection of law abiding and consuming citizens has broadly gone unchallenged. With little or no debate in the U.K. to critique the claims made by the burgeoning security industry that CCTV protects people in the form of a ‘Big Friend’, the state at both central and local levels has endorsed the installation of CCTV apparatus across the nation. Some areas assert in their promotional material that the centre of the shopping and leisure zone is fully surveilled by cameras in order to reassure visitors that their personal safety is a matter of civic concern, with even small towns and villages expending monies on sophisticated and expensive to maintain camera systems. It is within a context of monitoring, recording and control procedures that young people’s use of public space is constructed as a threat to social order, in need of surveillance and exclusion which forms a major and contemporary feature in shaping thinking about urban and rural working class young people in the U.K. As Loader (1996) notes, young people’s claims on public space rarely gain legitimacy if ‘colliding’ with those of local residents, and Davis (1990) describes the increasing ‘militarization and destruction of public space’, while Jacobs (1965) asserts that full participation in the ‘daily life of urban streets’ is essential to the development of young people and beneficial for all who live in an area. This paper challenges the uncritical acceptance of widespread use of CCTV and identifies its oppressive and malevolent potential in forming a ‘surveillance gaze’ over young people (adapting Foucault’s ‘clinical gaze’c. 1973) which can jeopardise mental health and well being in coping with the ‘metropolis’, after Simmel, (1964).

Towards a structured understanding of caregivers’ safety behaviour in the domestic and driveway setting

Slow speed run-overs represent a major cause of injury and death among Australian children, with higher rates of incidents being reported in Queensland than in the remaining Australian states. Yet, little attention has been given to how caregivers develop their safety behaviour in and around the driveway setting. To address this gap, the current study aimed to develop a conceptual model of driveway child safety behaviours among caregivers of children aged five years or younger. Semi-structured interviews were conducted with 26 caregivers (25 females/1 male, mean age, 33.24 year) from rural and metropolitan Queensland. To enable a comparison and validation of findings from the driveway, the study analysed both driveway and domestic safety behaviours. Domestic safety behaviours were categorised and validated against driveway safety behaviours, uncovering a process of risk appraisal and safety behaviour that was applicable in both settings (the Safety System Model). However, noteworthy differences between the domestic and driveway setting were uncovered. Unlike in the domestic setting, driveway risks were perceived as shifting according the presence of moving vehicles, which resulted in inconsistent safety behaviours. While the findings require further validation, they have implications for the design and implementation of driveway run-over interventions.

The financial life well-lived: psychological benefits of financial planning

As the financial planning industry undergoes a series of reforms aimed at increased professionalism and improved quality of advice, financial planner training in Australia and elsewhere has begun to acknowledge the importance of interdisciplinary knowledge bases in informing both curriculum design and professoinal practice (e.g. FPA2009). This paper underscores the importance of the process of financial planning by providing a conceptual analysis of the six step financial planning process using key mechanisms derived from theory and research in cognate disciplines such as psychology and well-being. The paper identifies how these mechanisms may operate to impact client well-being in the financial planning context. The conceptual mapping of th emechanisms to process elements of financial planning is a unique contribution to the financial planning literature and offers a further framework in the armamentarium of researchers interested in pursuing questions around the value of financial planning. The conceptual framework derived from the analysis also adds to the growing body of literature aimed at developing an integrated model of financial planning.