965 resultados para Data Interviews


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The wide range of contributing factors and circumstances surrounding crashes on road curves suggest that no single intervention can prevent these crashes. This paper presents a novel methodology, based on data mining techniques, to identify contributing factors and the relationship between them. It identifies contributing factors that influence the risk of a crash. Incident records, described using free text, from a large insurance company were analysed with rough set theory. Rough set theory was used to discover dependencies among data, and reasons using the vague, uncertain and imprecise information that characterised the insurance dataset. The results show that male drivers, who are between 50 and 59 years old, driving during evening peak hours are involved with a collision, had a lowest crash risk. Drivers between 25 and 29 years old, driving from around midnight to 6 am and in a new car has the highest risk. The analysis of the most significant contributing factors on curves suggests that drivers with driving experience of 25 to 42 years, who are driving a new vehicle have the highest crash cost risk, characterised by the vehicle running off the road and hitting a tree. This research complements existing statistically based tools approach to analyse road crashes. Our data mining approach is supported with proven theory and will allow road safety practitioners to effectively understand the dependencies between contributing factors and the crash type with the view to designing tailored countermeasures.

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Purpose: The purpose of this paper is to analyse the risk management process conducted by some private and not-for-profit affordable housing providers in South East Queensland, and draw conclusions about the relationship between risk assessments/responses and past experiences.----- Design/methodology/approach: In-depth interviews of selected non-government housing providers have been conducted to facilitate an understanding of their approach to risk assessment in developing and in managing affordable housing projects. Qualitative data are analysed using thematic analysis to find emerging themes suggested by interview participants.----- Findings: The paper finds that informal risk management process is used as part of normal business process in accordance with industry standards. Many interviewees agree that the recognition of financial risk and the fear of community rejection of such housing projects have restrained them from committing to such investment projects. The levels of acceptance of risk are not always consistent across housing providers which create opportunities to conduct multi-stakeholder partnership to reduce overall risk.----- Research limitations/implications: The paper has implications for developers or investors who seek to include affordable housing as part of their portfolio. However, data collected in the study are a cross-section of interviews that will not include the impact on recent tax incentives offers by the Australian Commonwealth Government.----- Practical implications: The study suggests that implementing improvements to the risk mitigation and management framework may assist in promoting the supply of affordable housing by non-government providers.----- Originality/value: The focus of the study is the interaction between partnerships and risk management in development and management of affordable rental housing.

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A data-driven background dataset refinement technique was recently proposed for SVM based speaker verification. This method selects a refined SVM background dataset from a set of candidate impostor examples after individually ranking examples by their relevance. This paper extends this technique to the refinement of the T-norm dataset for SVM-based speaker verification. The independent refinement of the background and T-norm datasets provides a means of investigating the sensitivity of SVM-based speaker verification performance to the selection of each of these datasets. Using refined datasets provided improvements of 13% in min. DCF and 9% in EER over the full set of impostor examples on the 2006 SRE corpus with the majority of these gains due to refinement of the T-norm dataset. Similar trends were observed for the unseen data of the NIST 2008 SRE.

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1. Ecological data sets often use clustered measurements or use repeated sampling in a longitudinal design. Choosing the correct covariance structure is an important step in the analysis of such data, as the covariance describes the degree of similarity among the repeated observations. 2. Three methods for choosing the covariance are: the Akaike information criterion (AIC), the quasi-information criterion (QIC), and the deviance information criterion (DIC). We compared the methods using a simulation study and using a data set that explored effects of forest fragmentation on avian species richness over 15 years. 3. The overall success was 80.6% for the AIC, 29.4% for the QIC and 81.6% for the DIC. For the forest fragmentation study the AIC and DIC selected the unstructured covariance, whereas the QIC selected the simpler autoregressive covariance. Graphical diagnostics suggested that the unstructured covariance was probably correct. 4. We recommend using DIC for selecting the correct covariance structure.

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This report provides an introduction to our analyses of secondary data with respect to violent acts and incidents relating to males living in rural settings in Australia. It clarifies important aspects of our overall approach primarily by concentrating on three elements that required early scoping and resolution. Firstly, a wide and inclusive view of violence which encompasses measures of violent acts and incidents and also data identifying risk taking behaviour and the consequences of violence is outlined and justified. Secondly, the classification used to make comparisons between the city and the bush together with associated caveats is outlined. The third element discussed is in relation to national injury data. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in five subsequent reports in this series.

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This report focuses on our examination of extant data which have been sourced with respect to self-harm and suicide in Australia. Moreover, specific areas of concern regarding elevated rates of suicide for rural males and data anomalies which emerged during our examination of these data are discussed. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in complementary reports in this series.

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This report focuses on our examination of extant data which have been sourced with respect to intentional violence perpetrated or experienced by males in regional and remote Australia. The nature of intentional violent acts can be physical, sexual or psychological or involve deprivation or neglect. We have presented under the headings of: self-harm including suicide; homicide; assault, sexual assault and the threat of assault; child abuse; other family and intimate partner violence; harassment, stalking and bullying; alcohol related social violence; and animal abuse. State variations in interpersonal violence are also presented. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in complementary reports in this series.

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This report focuses on our examination of extant data which have been sourced with respect to unintentional serious and violent injuries to males living in regional and remote Australia. Such injuries typically might be caused by, for example, transport accidents, occupational exposures and hazards, burns and so on. Thus unintentional violent incidents cause physical trauma the consequences of which can sometimes lead to chronic conditions including psychological harm or substance abuse. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in complementary reports in this series.

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This report focuses on our examination of extant data which have been sourced with respect to personally and socially risky behaviour associated with males living in regional and remote Australia . The AIHW (2008: PHE 97:89) defines personally risky behaviour, on the one hand, as working, swimming, boating, driving or operating hazardous machinery while intoxicated with alcohol or an illicit drug. Socially risky behaviour, on the other hand, is defined as creating a public disturbance, damaging property, stealing or verbally or physically abusing someone while intoxicated with alcohol or an illicit drug. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in complementary reports in this series.

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This report considers extant data which have been sourced with respect to some of the consequences of violent acts and incidents and risky behaviour for males living in regional and remote Australia . This has been collated and presented under the headings: juvenile offenders; long-term health consequences; anxiety and repression; and other chronic disabilities. Additional commentary resulting from exploration, examination and analyses of secondary data is published online in complementary reports in this series.

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The School Based Youth Health Nurse Program was established in 1999 by the Queensland Government to fund school nurse positions in Queensland state high schools. Schools were required to apply for a School Based Youth Health Nurse during a five-phase recruitment process, managed by the health districts, and rolled out over four years. The only mandatory selection criterion for the position of School Based Youth Health Nurse was registration as a General Nurse and most School Based Youth Health Nurses are allocated to two state high schools. Currently, there are approximately 115 Full Time Equivalent School Based Youth Health Nurse positions across all Queensland state high schools. The literature review revealed an abundance of information about school nursing. Most of the literature came from the United Kingdom and the United States, who have a different model of school nursing to school based youth health nursing. However, there is literature to suggest school nursing is gradually moving from a disease-focused approach to a social view of health. The noticeable number of articles about, for example, drug and alcohol, mental health, and contemporary sexual health issues, is evidence of this change. Additionally, there is a significant the volume of literature about partnerships and collaboration, much of which is about health education, team teaching and how school nurses and schools do health business together. The surfacing of this literature is a good indication that school nursing is aligning with the broader national health priority areas. More particularly, the literature exposed a small but relevant and current body of research, predominantly from Queensland, about school based youth health nursing. However, there remain significant gaps in the knowledge about school based youth health nursing. In particular, there is a deficit about how School Based Youth Heath Nurses understand the experience of school based youth health nursing. This research aimed to reveal the meaning of the experience of school based youth health nursing. The research question was How do School Based Youth Health Nurses’ understand the experience of school based youth health nursing? This enquiry was instigated because the researcher, who had a positive experience of school based youth health nursing, considered it important to validate other School Based Youth Health Nurses’ experiences. Consequently, a comprehensive use of qualitative research was considered the most appropriate manner to explore this research question. Within this qualitative paradigm, the research framework consists of the epistemology of social constructionism, the theoretical perspective of interpretivism and the approach of phenomenography. After ethical approval was gained, purposeful and snowball sampling was used to recruit a sample of 16 participants. In-depth interviews, which were voluntary, confidential and anonymous, were mostly conducted in public venues and lasted from 40-75 minutes. The researcher also kept a researchers journal as another form of data collection. Data analysis was guided by Dahlgren and Fallsbergs’ (1991, p. 152) seven phases of data analysis which includes familiarization, condensation, comparison, grouping, articulating, labelling and contrasting. The most important finding in this research is the outcome space, which represents the entirety of the experience of school based youth health nursing. The outcome space consists of two components: inside the school environment and outside the school environment. Metaphorically and considered as whole-in-themselves, these two components are not discreet but intertwined with each other. The outcome space consists of eight categories. Each category of description is comprised of several sub-categories of description but as a whole, is a conception of school based youth health nursing. The eight conceptions of school based youth health nursing are: 1. The conception of school based youth health nursing as out there all by yourself. 2. The conception of school based youth health nursing as no real backup. 3. The conception of school based youth health nursing as confronted by many barriers. 4. The conception of school based youth health nursing as hectic and full-on. 5. The conception of school based youth health nursing as working together. 6. The conception of school based youth health nursing as belonging to school. 7. The conception of school based youth health nursing as treated the same as others. 8. The conception of school based youth health nursing as the reason it’s all worthwhile. These eight conceptions of school based youth health nursing are logically related and form a staged hierarchical relationship because they are not equally dependent on each other. The conceptions of school based youth health nursing are grouped according to negative, negative and positive and positive conceptions of school based youth health nursing. The conceptions of school based youth health nursing build on each other, from the bottom upwards, to reach the authorized, or the most desired, conception of school based youth health nursing. This research adds to the knowledge about school nursing in general but especially about school based youth health nursing specifically. Furthermore, this research has operational and strategic implications, highlighted in the negative conceptions of school based youth health nursing, for the School Based Youth Health Nurse Program. The researcher suggests the School Based Youth Health Nurse Program, as a priority, address the operational issues The researcher recommends a range of actions to tackle issues and problems associated with accommodation and information, consultations and referral pathways, confidentiality, health promotion and education, professional development, line management and School Based Youth Health Nurse Program support and school management and community. Strategically, the researcher proposes a variety of actions to address strategic issues, such as the School Based Youth Health Nurse Program vision, model and policy and practice framework, recruitment and retention rates and evaluation. Additionally, the researcher believes the findings of this research have the capacity to spawn a myriad of future research projects. The researcher has identified the most important areas for future research as confidentiality, information, qualifications and health outcomes.

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Despite the facts that vehicle incidents continue to be the most common mechanism for Australian compensated fatalities and that employers have statutory obligations to provide safe workplaces, very few organisations are proactively and comprehensively managing their work-related road risks. Unfortunately, limited guidance is provided in the existing literature to assist practitioners in managing work-related road risks. The current research addresses this gap in the literature. To explore how work-related road safety can be enhanced, three studies were conducted. Study one explored the effectiveness of a range of risk management initiatives and whether comprehensive risk management practices were associated with safety outcomes. Study two explored barriers to, and facilitators for, accepting risk management initiatives. Study three explored the influence of organisational factors on road safety outcomes to identify optimal work environments for managing road risks. To maximise the research sample and increase generalisability, the studies were designed to allow data collection to be conducted simultaneously drawing upon the same sample obtained from four Australian organisations. Data was collected via four methods. A structured document review of published articles was conducted to identify what outcomes have been observed in previously investigated work-related road safety initiatives. The documents reviewed collectively assessed the effectiveness of 19 work-related road safety initiatives. Audits of organisational practices and process operating within the four researched organisations were conducted to identify whether organisations with comprehensive work-related road risk management practices and processes have better safety outcomes than organisations with limited risk management practices and processes. Interviews were conducted with a sample of 24 participants, comprising 16 employees and eight managers. The interviews were conducted to identify what barriers and facilitators within organisations are involved in implementing work-related road safety initiatives and whether differences in fleet safety climate, stage of change and safety ownership relate to work-related road safety outcomes. Finally, questionnaires were administered to a sample of 679 participants. The questionnaires were conducted to identify which initiatives are perceived by employees to be effective in managing work-related road risks and whether differences in fleet safety climate, stage of change and safety ownership relate to work-related road safety outcomes. Seven research questions were addressed in the current research project. The key findings with respect to each of the research questions are presented below. Research question one: What outcomes have been observed in previously investigated work-related road safety initiatives? The structured document review indicated that initiatives found to be positively associated with occupational road safety both during and after the intervention period included: a pay rise; driver training; group discussions; enlisting employees as community road safety change agents; safety reminders; and group and individual rewards. Research question two: Which initiatives are perceived by employees to be effective in managing work-related road risks? Questionnaire findings revealed that employees believed occupational road risks could best be managed through making vehicle safety features standard, providing practical driver skills training and through investigating serious vehicle incidents. In comparison, employees believed initiatives including signing a promise card commitment to drive safely, advertising the organisation’s phone number on vehicles and consideration of driving competency in staff selection process would have limited effectiveness in managing occupational road safety. Research question three: Do organisations with comprehensive work-related road risk management practices and processes have better safety outcomes than organisations with limited risk management practices and processes? The audit identified a difference among the organisations in their management of work-related road risks. Comprehensive risk management practices were associated with employees engaging in overall safer driving behaviours, committing less driving errors, and experiencing less fatigue and distraction issues when driving. Given that only four organisations participated in this research, these findings should only be considered as preliminary. Further research should be conducted to explore the relationship between comprehensiveness of risk management practices and road safety outcomes with a larger sample of organisations. Research question four: What barriers and facilitators within organisations are involved in implementing work-related road safety initiatives? The interviews identified that employees perceived six organisational characteristics as potential barriers to implementing work-related road safety initiatives. These included: prioritisation of production over safety; complacency towards work-related road risks; insufficient resources; diversity; limited employee input in safety decisions; and a perception that road safety initiatives were an unnecessary burden. In comparison, employees perceived three organisational characteristics as potential facilitators to implementing work-related road safety initiatives. These included: management commitment; the presence of existing systems that could support the implementation of initiatives; and supportive relationships. Research question five: Do differences in fleet safety climate relate to work-related road safety outcomes? The interviews and questionnaires identified that organisational climates with high management commitment, support for managing work demands, appropriate safety rules and safety communication were associated with employees who engaged in safer driving behaviours. Regression analyses indicated that as participants’ perceptions of safety climate increased, the corresponding likelihood of them engaging in safer driving behaviours increased. Fleet safety climate was perceived to influence road safety outcomes through several avenues. Some of these included: the allocation of sufficient resources to manage occupational road risks; fostering a supportive environment of mutual responsibility; resolving safety issues openly and fairly; clearly communicating to employees that safety is the top priority; and developing appropriate work-related road safety policies and procedures. Research question six: Do differences in stage of change relate to work-related road safety outcomes? The interviews and questionnaires identified that participants’ perceptions of initiative effectiveness were found to vary with respect to their individual stage of readiness, with stage-matched initiatives being perceived most effective. In regards to safety outcomes, regression analyses identified that as participants’ progress through the stages of change, the corresponding likelihood of them being involved in vehicle crashes decreases. Research question seven: Do differences in safety ownership relate to work-related road safety outcomes? The interviews and questionnaires revealed that management of road risks is often given less attention than other areas of health and safety management in organisations. In regards to safety outcomes, regression analyses identified that perceived authority and perceived shared ownership both emerged as significant independent predictors of self-reported driving behaviours pertaining to fatigue and distractions. The regression models indicated that as participants’ perceptions of the authority of the person managing road risks increases, and perceptions of shared ownership of safety tasks increases, the corresponding likelihood of them engaging in driving while fatigued or multitasking while driving decreases. Based on the findings from the current research, the author makes several recommendations to assist practitioners in developing proactive and comprehensive approaches to managing occupational road risks. The author also suggests several avenues for future research in the area of work-related road safety.

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The ability to forecast machinery failure 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 for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

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Established Monte Carlo user codes BEAMnrc and DOSXYZnrc permit the accurate and straightforward simulation of radiotherapy experiments and treatments delivered from multiple beam angles. However, when an electronic portal imaging detector (EPID) is included in these simulations, treatment delivery from non-zero beam angles becomes problematic. This study introduces CTCombine, a purpose-built code for rotating selected CT data volumes, converting CT numbers to mass densities, combining the results with model EPIDs and writing output in a form which can easily be read and used by the dose calculation code DOSXYZnrc. The geometric and dosimetric accuracy of CTCombine’s output has been assessed by simulating simple and complex treatments applied to a rotated planar phantom and a rotated humanoid phantom and comparing the resulting virtual EPID images with the images acquired using experimental measurements and independent simulations of equivalent phantoms. It is expected that CTCombine will be useful for Monte Carlo studies of EPID dosimetry as well as other EPID imaging applications.