Postpartum fatigue and driving : relating experiences, thoughts and opinions 12 weeks post-birth

Fatigue in the postnatal period is such a common experience for most mothers that the term ‘postpartum fatigue’ (PPF) has been coined to describe it. When new mothers experience extreme fatigue, it follows that their physical health, mental health, and social-wellbeing is negatively affected. It is interesting to note that there is a distinct lack of empirical investigations focusing on the link between PPF and increased risk of injury; particularly when the links between fatigue and increased risk of road crashes are well documented. The purpose of this investigation was to undertake pilot research to develop an understanding of the duration of PPF and the performance impairments experienced by new mothers when involved in safety-sensitive activities, such as driving a motor vehicle. Semi-structured interviews were undertaken with women (N = 24) at 12 weeks postpartum living in South-east Queensland, Australia. Key themes were identified; with a particular emphasis towards understanding the link between the participant’s experience of postpartum fatigue and the impact this has on their overall cognitive and physiological functioning, as well as their experience of the driving task. Further, sleep/wake data was collected and using the Karolinska Sleepiness Scale (KSS) the potential crash risk for this group of mothers is discussed. It is proposed that the findings of this investigation could be used to improve current knowledge among new mothers and practitioners regarding the mechanisms and consequences of fatigue and to inform interventions that lead to a decreased risk of injury associated with postpartum fatigue.

60Sox Report Volume 1: from education to work in Australia's digital content industries. The opinions and practices of aspiring creatives in the creative industries

This report presents findings from the largest survey of aspiring creatives who work or intend to work in the digital content industries ever undertaken in Australia. Survey respondents included those with aspirations to work in the publicly-supported, less commercial end of the Creative Industries spectrum as well as those with aspirations to work in the digital content industries. The survey gathered rich data on their characteristics, skills and attributes, barriers to employment, workforce mobility, career intentions, professional development, mentors and industry supports, and participation in communities of practice. The survey sought to determine if aspiring creatives have the necessary skills and attributes to work effectively in the digital content industries. This task also involved finding out how they develop their skills and attributes, and what they need to develop them further.

From education to work in Australia’s creative digital industries : comparing the opinions and practices of employers and aspiring creatives

This report represents the second of two reports that aim to explore views about the employability of aspiring creatives. The first report, released in June 2009, presented findings from a survey of 507 aspiring creatives, defined as recent graduates and/or people with less than two years industry experience. It presents findings from a project that administered an survey to employers in Australia’s Creative Digital Industries. The survey included questions on employer characteristics, recruitment and training practices, employers’ views of the capabilities of aspiring creatives, and participation in communities of interest/networks, mentoring and internships. The main purpose of the project was to identify capability gaps of aspiring creatives as well as those factors that enhance or inhibit employers’ views of the capabilities of aspiring creatives – both of which impact on the ability of aspiring creatives to find work in their preferred occupations in Australia’s Creative Digital Industries.

Combining expert opinions in prior elicitation

A method of eliciting prior distributions for Bayesian models using expert knowledge is proposed. Elicitation is a widely studied problem, from a psychological perspective as well as from a statistical perspective. Here, we are interested in combining opinions from more than one expert using an explicitly model-based approach so that we may account for various sources of variation affecting elicited expert opinions. We use a hierarchical model to achieve this. We apply this approach to two problems. The first problem involves a food risk assessment problem involving modelling dose-response for Listeria monocytogenes contamination of mice. The second concerns the time taken by PhD students to submit their thesis in a particular school.

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Opinions, attitudes and driving experiences of car and racing "enthusiasts" : results of the Ontario Car and Racing Club Survey

In recent years, car club and racing websites and forums have become an increasingly popular way for car enthusiasts to access racing and car club news, chat-rooms and message boards. However, no North American research has been found that has examined opinions and driving experiences of car and racing enthusiasts. The purpose of this study was to examine car club members’ opinions about and experiences with various aspects of driving, road safety and traffic legislation, with a particular focus on street racing. A web-based questionnaire (Survey Monkey) was developed using the expert panel method and was primarily based on validated instruments or questions that were developed from other surveys. The questionnaire included: 1) driver concerns regarding traffic safety issues and legislation; 2) attitudes regarding various driving activities; 3) leisure-time activities, including club activities; 4) driving experiences, including offences and collisions; and 5) socio-demographic questions. The survey was pre- tested and piloted. Electronic information letters were sent out to an identified list of car clubs and forums situated in southern Ontario. Car club participants were invited to fill out the questionnaire. This survey found that members of car clubs share similar concerns regarding various road safety issues with samples of Canadian drivers, although a smaller percentage of car club members are concerned about speeding-related driving. Car club members had varied opinions regarding Ontario’s Street Racers, Stunt and Aggressive Drivers Legislation. The respondents agreed the most with the new offences regarding not sitting in the driver’s seat, having a person in the trunk, or driving as close as possible to another vehicle, pedestrian or object on or near the highway without a reason. The majority disagreed with police powers of impoundment and on-the-spot licence suspensions. About three quarters of respondents reported no collisions or police stops for traffic offences in the past five years. Of those who had been stopped, the most common offence was reported as speeding. This study is the first in Canada to examine car club members’ opinions about and experiences with various aspects of driving, road safety and traffic legislation. Given the ubiquity of car clubs and fora in Canada, insights on members’ opinions and practices provide important information to road safety researchers.

Sentencing and public confidence : results from a national Australian survey on public opinions towards sentencing

This paper examines the critical issue of public confidence in sentencing, and presents findings from Phase I of an Australia-wide sentencing and public confidence project. Phase I comprised a nationally representative telephone survey of 6005 participants. The majority of respondents expressed high levels of punitiveness and were dissatisfied with sentences imposed by the courts. Despite this, many were strongly supportive of the use of alternatives to imprisonment for a range of offences. These nuanced views raise questions regarding the efficacy of gauging public opinion using opinion poll style questions; indeed the expected outcome from this first phase of the four phase sentencing and public confidence project. The following phases of this project, reported on elsewhere, examined the effects of various interventions on the robustness and nature of these views initially expressed in a standard ‘top of the head’ opinion poll.

Oscar Pistorius and the death of Reeva Steenkamp : mapping public opinions on Twitter using frame analysis and data mining

This paper uses innovative content analysis techniques to map how the death of Oscar Pistorius' girlfriend, Reeva Steenkamp, was framed on Twitter conversations. Around 1.5 million posts from a two-week timeframe are analyzed with a combination of syntactic and semantic methods. This analysis is grounded in the frame analysis perspective and is different than sentiment analysis. Instead of looking for explicit evaluations, such as “he is guilty” or “he is innocent”, we showcase through the results how opinions can be identified by complex articulations of more implicit symbolic devices such as examples and metaphors repeatedly mentioned. Different frames are adopted by users as more information about the case is revealed: from a more episodic one, highly used in the very beginning, to more systemic approaches, highlighting the association of the event with urban violence, gun control issues, and violence against women. A detailed timeline of the discussions is provided.

Combining expert opinions in prior elicitation

We consider the problem of combining opinions from different experts in an explicitly model-based way to construct a valid subjective prior in a Bayesian statistical approach. We propose a generic approach by considering a hierarchical model accounting for various sources of variation as well as accounting for potential dependence between experts. We apply this approach to two problems. The first problem deals with a food risk assessment problem involving modelling dose-response for Listeria monocytogenes contamination of mice. Two hierarchical levels of variation are considered (between and within experts) with a complex mathematical situation due to the use of an indirect probit regression. The second concerns the time taken by PhD students to submit their thesis in a particular school. It illustrates a complex situation where three hierarchical levels of variation are modelled but with a simpler underlying probability distribution (log-Normal).

Consumer misbehavior: a concurrent look at the impact that the size of the victim and size of the loss have on opinions regarding the acceptance or unacceptance of 12 questionable consumer actions

This study examines the role that the size of a victimised organisation and the size of the victim’s loss have on attitudes regarding the acceptance or unacceptance of 12 questionable consumer actions. A sample of 815 American adults rated each scenario on a scale anchored by very acceptable and very unacceptable. It was shown that the size of the victimised organisation tends to influence consumers’ opinions with more disdain directed towards consumers who take advantage of smaller businesses. Similarly, the respondents tended to be more critical of these actions when the loss incurred by the victimised organisation was large. A 2x2 matrix concurrently delineated the nature of the extent to which opinions regarding the 12 actions differed depending upon the mediating variable under scrutiny.

Combining opinions for use in Bayesian networks: A measurement error approach

Bayesian networks (BNs) are graphical probabilistic models used for reasoning under uncertainty. These models are becoming increasing popular in a range of fields including ecology, computational biology, medical diagnosis, and forensics. In most of these cases, the BNs are quantified using information from experts, or from user opinions. An interest therefore lies in the way in which multiple opinions can be represented and used in a BN. This paper proposes the use of a measurement error model to combine opinions for use in the quantification of a BN. The multiple opinions are treated as a realisation of measurement error and the model uses the posterior probabilities ascribed to each node in the BN which are computed from the prior information given by each expert. The proposed model addresses the issues associated with current methods of combining opinions such as the absence of a coherent probability model, the lack of the conditional independence structure of the BN being maintained, and the provision of only a point estimate for the consensus. The proposed model is applied an existing Bayesian Network and performed well when compared to existing methods of combining opinions.

Prior and posterior linear pooling for combining expert opinions: Uses and impact on Bayesian Networks

The use of expert knowledge to quantify a Bayesian Network (BN) is necessary when data is not available. This however raises questions regarding how opinions from multiple experts can be used in a BN. Linear pooling is a popular method for combining probability assessments from multiple experts. In particular, Prior Linear Pooling (PrLP), which pools opinions then places them into the BN is a common method. This paper firstly proposes an alternative pooling method, Posterior Linear Pooling (PoLP). This method constructs a BN for each expert, then pools the resulting probabilities at the nodes of interest. Secondly, it investigates the advantages and disadvantages of using these pooling methods to combine the opinions of multiple experts. Finally, the methods are applied to an existing BN, the Wayfinding Bayesian Network Model, to investigate the behaviour of different groups of people and how these different methods may be able to capture such differences. The paper focusses on 6 nodes Human Factors, Environmental Factors, Wayfinding, Communication, Visual Elements of Communication and Navigation Pathway, and three subgroups Gender (female, male),Travel Experience (experienced, inexperienced), and Travel Purpose (business, personal) and finds that different behaviors can indeed be captured by the different methods.