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.

Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.

Matrix regularization techniques for online multitask learning

In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks. We show how this corresponds naturally to learning under spectral or structural matrix constraints, and propose regularization techniques to enforce the constraints. The regularization techniques proposed here are interesting in their own right and multitask learning is just one application for the ideas. A theoretical analysis of one such regularizer is performed, and a regret bound that shows benefits of this setup is reported.