Addressing sustainable living using a collaborative approach and multi-disciplinary techniques

In order to achieve meaningful reductions in individual ecological footprints, individuals must dramatically alter their day to day behaviours. Effective interventions will need to be evidence based and there is a necessity for the rapid transfer or communication of information from the point of research, into policy and practice. A number of health disciplines, including psychology and public health, share a common mission to promote health and well-being and it is becoming clear that the most practical pathway to achieving this mission is through interdisciplinary collaboration. This paper argues that an interdisciplinary collaborative approach will facilitate research that results in the rapid transfer of findings into policy and practice. The application of this approach is described in relation to the Green Living project which explored the psycho-social predictors of environmentally friendly behaviour. Following a qualitative pilot study, and in consultation with an expert panel comprising academics, industry professionals and government representatives, a self-administered mail survey was distributed to a random sample of 3000 residents of Brisbane and Moreton Bay (Queensland, Australia). The Green Living survey explored specific beliefs which included attitudes, norms, perceived control, intention and behaviour, as well as a number of other constructs such as environmental concern and altruism. This research has two beneficial outcomes. First, it will inform a practical model for predicting sustainable living behaviours and a number of local councils have already expressed an interest in making use of the results as part of their ongoing community engagement programs. Second, it provides an example of how a collaborative interdisciplinary project can provide a more comprehensive approach to research than can be accomplished by a single disciplinary project.

A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques

Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.

Narrative therapy for adults with major depressive disorder: Improved symptom and interpersonal outcomes

Abstract This study investigated depressive symptom and interpersonal relatedness outcomes from eight sessions of manualized narrative therapy for 47 adults with major depressive disorder. Post-therapy, depressive symptom improvement (d=1.36) and proportions of clients achieving reliable improvement (74%), movement to the functional population (61%), and clinically significant improvement (53%) were comparable to benchmark research outcomes. Post-therapy interpersonal relatedness improvement (d=.62) was less substantial than for symptoms. Three-month follow-up found maintenance of symptom, but not interpersonal gains. Benchmarking and clinical significance analyses mitigated repeated measure design limitations, providing empirical evidence to support narrative therapy for adults with major depressive disorder. RÉSUMÉ Cette étude a investigué les symptômes dépressifs et les relations interpersonnels d'une thérapie narrative en huit séances chez 47 adultes souffrant d'un trouble dépressif majeur. Après la thérapie, l'amélioration des symptômes dépressifs (d=1.36) et la proportion de clients atteignant un changement significatif (74%), le mouvement vers la population fonctionnelle (61%), enfin l'amélioration clinique significative (53%) étaient comparables aux performances des études de résultats. L'amélioration des relations interpersonnelles (d=0.62) était inférieure à l'amélioration symptomatique. Le suivi à trois mois montrait un maintien des gains symptomatiques mais pas pour les relations interpersonnelles. L’évaluation des performances et les analyses de significativité clinique modèrent les limitations du plan de recherche à mesures répétées et apportent une preuve empirique qui étaie l'efficacité des thérapies narratives pour des adultes avec un trouble dépressif majeur. Este estudo investigou sintomas depressivos e resultados interpessoais relacionados em oito sessões de terapia narrativa manualizada para 47 adultos com perturbação depressiva major. No pós terapia, melhoria de sintomas depressivos (d=1,36) e proporção de clientes que alcançam melhoria válida (74%), movimento para a população funcional (61%) e melhoria clinicamente significativa (53%) foram comparáveis com os resultados da investigação reportados. As melhorias pós terapia nos resultados interpessoais relacionados (d=.62) foi menos substancial do que para os sintomas. Aos três meses de seguimento houve a manutenção dos sintomas mas não dos ganhos interpessoais. As análises de benchemarking e de melhoria clinicamente significativas atenuam as limitações de um design de medidas repetidas, fornecendo evidência empírica para a terapia narrativa para adultos com perturbação depressiva major. Questo lavoro ha valutato i sintomi depressivi e gli outcome nella capacità di relazionarsi a livello interpersonale in 8 sedute di psicoterapia narrativa manualizzata in un gruppo di 47 adulti con depressione maggiore. I risultati ottenuti relativamente a: post terapy, miglioramento dei sintomi depressivi (d_1.36), proporzione di pazienti che hanno raggiunto un miglioramento affidabile e consistente (74%), movimento verso il funzionamento atteso nella popolazione (61%) e miglioramento clinicamente significativo (53%) sono paragonabili ai valori di riferimento della ricerca sull'outcome. I miglioramento della capacità di relazionarsi valutata alla fine del trattamento (d_.62) si è rivelata meno sostanziale rispetto ai sintomi. Un follow-up dopo 3 mesi ha dimostrato che il miglioramento sintomatologico è stato mantenuto, ma non quello degli obiettivi interpersonali. Valori di riferimento e analisi della significatività clinica hanno fatto fronte ai limiti del disegno a misure ripetute, offrendo prove empiriche sulla rilevanza della terapia narrativa in pazienti adulti con depressione maggiore

The link between belongingness and depressive symptoms: An exploration in the workplace interpersonal context

Interpersonal factors are crucial to a deepened understanding of depression. Belongingness, also referred to as connectedness, has been established as a strong risk/protective factor for depressive symptoms. To elucidate this link it may be beneficial to investigate the relative importance of specific psychosocial contexts as belongingness foci. Here we investigate the construct of workplace belongingness. Employees at a disability services organisation (N = 125) completed measures of depressive symptoms, anxiety symptoms, workplace belongingness and organisational commitment. Psychometric analyses, including Horn's parallel analyses, indicate that workplace belongingness is a unitary, robust and measurable construct. Correlational data indicate a substantial relationship with depressive symptoms (r = −.54) and anxiety symptoms (r = −.39). The difference between these correlations was statistically significant, supporting the particular importance of belongingness cognitions to the etiology of depression. Multiple regression analyses support the hypothesis that workplace belongingness mediates the relationship between affective organisational commitment and depressive symptoms. It is likely that workplaces have the potential to foster environments that are intrinsically less depressogenic by facilitating workplace belongingness. From a clinical perspective, cognitions regarding the workplace psychosocial context appear to be highly salient to individual psychological health, and hence warrant substantial attention.

Mood and interpersonal functioning in heavy smokers

Male and female adult heavy smokers (n = 96) and non-smokers (n = 123) were compared on the Depression Anxiety Stress Scales (DASS), Adult Attachment Scale (AAS), Fear of Intimacy Scale (FIS), Negative Mood Regulation (NMR) Scale and Affect Intensity Measure (AIM). Compared with non-smokers, smokers scored significantly higher on DASS-Stress, DASS-Anxiety, and DASS-Depression, and significantly lower on NMR, AAS-Depend and AAS-Close. Smokers also scored marginally higher on FIS. Results suggest mood and relationship dysfunction in smokers, similar to the findings of a previous investigation of detoxified inpatients undergoing treatment for substance (alcohol, heroin, or methamphetamine) dependence.

Live my work : rural nurses and their multiple perspectives of self

Aim. This paper is a report of a study to explore rural nurses' experiences of mentoring. Background. Mentoring has recently been proposed by governments, advocates and academics as a solution to the problem for retaining rural nurses in the Australian workforce. Action in the form of mentor development workshops has changed the way that some rural nurses now construct supportive relationships as mentoring. Method. A grounded theory design was used with nine rural nurses. Eleven semi-structured interviews were conducted in various states of Australia during 2004-2005. Situational analysis mapping techniques and frame analysis were used in combination with concurrent data generation and analysis and theoretical sampling. Findings. Experienced rural nurses cultivate novices through supportive mentoring relationships. The impetus for such relationships comes from their own histories of living and working in the same community, and this was termed 'live my work'. Rural nurses use multiple perspectives of self in order to manage their interactions with others in their roles as community members, consumers of healthcare services and nurses. Personal strategies adapted to local context constitute the skills that experienced rural nurses pass-on to neophyte rural nurses through mentoring, while at the same time protecting them through troubleshooting and translating local cultural norms. Conclusion. Living and working in the same community creates a set of complex challenges for novice rural nurses that are better faced with a mentor in place. Thus, mentoring has become an integral part of experienced rural nurses' practice to promote staff retention. © 2007 The Authors.