Does vicarious traumatisation affect oncology nurses?:A literature review

It is widely documented that nurses experience work-related stress [Quine, L., 1998. Effects of stress in an NHS trust: a study. Nursing Standard 13 (3), 36-41; Charnley, E., 1999. Occupational stress in the newly qualified staff nurse. Nursing Standard 13 (29), 32-37; McGrath, A., Reid, N., Boore, J., 2003. Occupational stress in nursing. International Journal of Nursing Studies 40, 555-565; McVicar, A., 2003. Workplace stress in nursing: a literature review. Journal of Advanced Nursing 44 (6), 633-642; Bruneau, B., Ellison, G., 2004. Palliative care stress in a UK community hospital: evaluation of a stress-reduction programme. International Journal of Palliative Nursing 10 (6), 296-304; Jenkins, R., Elliott, P., 2004. Stressors, burnout and social support: nurses in acute mental health settings. Journal of Advanced Nursing 48 (6), 622-631], with cancer nursing being identified as a particularly stressful occupation [Hinds, P.S., Sanders, C.B., Srivastava, D.K., Hickey, S., Jayawardene, D., Milligan, M., Olsen, M.S., Puckett, P., Quargnenti, A., Randall, E.A., Tyc, V., 1998. Testing the stress-response sequence model in paediatric oncology nursing. Journal of Advanced Nursing 28 (5), 1146-1157; Barnard, D., Street, A., Love, A.W., 2006. Relationships between stressors, work supports and burnout among cancer nurses. Cancer Nursing 29 (4), 338-345]. Terminologies used to capture this stress are burnout [Pines, A.M., and Aronson, E., 1988. Career Burnout: Causes and Cures. Free Press, New York], compassion stress [Figley, C.R., 1995. Compassion Fatigue. Brunner/Mazel, New York], emotional contagion [Miller, K.I., Stiff, J.B., Ellis, B.H., 1988. Communication and empathy as precursors to burnout among human service workers. Communication Monographs 55 (9), 336-341] or simply the cost of caring (Figley, 1995). However, in the mental health field such as psychology and counselling, there is terminology used to captivate this impact, vicarious traumatisation. Vicarious traumatisation is a process through which the therapist's inner experience is negatively transformed through empathic engagement with client's traumatic material [Pearlman, L.A., Saakvitne, K.W., 1995a. Treating therapists with vicarious traumatization and secondary traumatic stress disorders. In: Figley, C.R. (Ed.), Compassion Fatigue: Coping with Secondary Traumatic Stress Disorder in Those Who Treat the Traumatized. Brunner/Mazel, New York, pp. 150-177]. Trauma not only affects individuals who are primarily present, but also those with whom they discuss their experience. If an individual has been traumatised as a result of a cancer diagnosis and shares this impact with oncology nurses, there could be a risk of vicarious traumatisation in this population. However, although Thompson [2003. Vicarious traumatisation: do we adequately support traumatised staff? The Journal of Cognitive Rehabilitation 24-25] suggests that vicarious traumatisation is a broad term used for workers from any profession, it has not yet been empirically determined if oncology nurses experience vicarious traumatisation. This purpose of this paper is to introduce the concept of vicarious traumatisation and argue that it should be explored in oncology nursing. The review will highlight that empirical research in vicarious traumatisation is largely limited to the mental health professions, with a strong recommendation for the need to empirically determine whether this concept exists in oncology nursing.

Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities

Discrete Conditional Phase-type (DC-Ph) models consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian Phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.

Models of Mental Health

A good understanding of the different theoretical models is essential when working in the field of mental health. Not only does it help with understanding experiences of mental health difficulties and to find meaning, but it also provides a framework for expanding our knowledge of the field.

As part of the Foundations of Mental Health Practice series, this book provides a critical overview of the theoretical perspectives relevant to mental health practice. At the core of this book is the idea that no single theory is comprehensive on its own and each theory has its limitations. Divided in to two parts, Part I explores traditional models of mental health and covers the key areas: bio-medical perspectives, psychological perspectives and social perspectives, whilst Part II looks at contemporary ideas that challenge and push these traditional views. The contributions, strengths and limitations of each model are explored and, as a result, the book encourages a more holistic, open approach to understanding and responding to mental health issues.

Together, these different approaches offer students and practitioners a powerful set of perspectives from which to approach their study and careers. Each model is covered in a clear and structured way with supporting exercises and case studies. It is an essential text for anyone studying or practising in the field of mental health, including social workers, nurses and psychologists.

Fast and frugal models of clinical judgement in novice and expert physicians

Our objective was to study whether “compensatory” models provide better descriptions of clinical judgment than fast and frugal models, according to expertise and experience. Fifty practitioners appraised 60 vignettes describing a child with an exacerbation of asthma and rated their propensities to admit the child. Linear logistic (LL) models of their judgments were compared with a matching heuristic (MH) model that searched available cues in order of importance for a critical value indicating an admission decision. There was a small difference between the 2 models in the proportion of patients allocated correctly (admit or not-admit decisions), 91.2% and 87.8%, respectively. The proportion allocated correctly by the LL model was lower for consultants than juniors, whereas the MH model performed equally well for both. In this vignette study, neither model provided any better description of judgments made by consultants or by pediatricians compared to other grades and specialties.

Widening our knowledge concerning young looked after children: the case for research using sociological models of childhood

The Assessment and Action framework for looked after children, designed to improve outcomes for all children in public care and those at home on care orders, is now well established in the UK. This paper offers a critical evaluation of the framework by examining the model of childhood upon which it is premised and by exploring its relationship to children's rights as conceptualized in the United Nations Convention on the Rights of the Child (1989). It will be argued that the particular child development model which underpins the framework addresses the rights of looked after children to protection and provision but does not allow for their participation rights to be sufficiently addressed. A critical review of the research concerning the education and health of looked after children is used to illustrate these points. It will be argued that what are missing are the detailed accounts of looked after children themselves. It is concluded that there is a need for the development of additional research approaches premised upon sociological models of childhood. These would allow for a greater engagement with the participation rights of this group of children and complement the pre-existing research agenda

Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions

Modelling patient flow in health care systems is vital in understanding the system activity and may therefore prove to be useful in improving their functionality. An extensively used measure is the average length of stay which, although easy to calculate and quantify, is not considered appropriate when the distribution is very long-tailed. In fact, simple deterministic models are generally considered inadequate because of the necessity for models to reflect the complex, variable, dynamic and multidimensional nature of the systems. This paper focuses on modelling length of stay and flow of patients. An overview of such modelling techniques is provided, with particular attention to their impact and suitability in managing a hospital service.

Willingness to use functional breads. Applying the Health Belief Model across four European countries

The present study focused on the role of the Health Belief Model (HBM) in predicting willingness to use functional breads, across four European countries: UK (N = 552), Italy (N = 504), Germany (N = 525) and Finland (N = 513). The behavioural evaluation components of the HBM (the perceived benefits and barriers conceptualized respectively as perceived healthiness and pleasantness) and the health motivation component were good predictors of willingness to use functional breads whereas threat perception components (perceived susceptibility and perceived anticipated severity) failed as predictors. This result was common in all four countries and across products. The role of 'cue to action' was marginal. On the whole the HBM fit was similar across the countries and products in terms of significant predictors (the perceived benefits, barriers and health motivation) with the exception of self-efficacy which was significant only in Finland. Young consumers seemed more interested in the functional bread with a health claim promoting health rather than in reducing risk of disease, whereas the opposite was true for older people. However, functional staple foods, such as bread in this European study, are still perceived as common foods rather than as a means of avoiding diseases. Consumers seek these foods for their healthiness (the perceived benefits) as they expect them to be healthier than regular foods and for the pleasantness (the perceived barriers) as they do not expect any change in the sensory characteristics due to the addition of the functional ingredients. The importance of health motivation in willingness to use products with health claims implies that there is an opening for developing better models for explaining health-promoting food choices that take into account both food and health-related factors without making a reference to disease-related outcome. (C) 2008 Elsevier Ltd. All rights reserved.

Assessment of the benefits of Discrete Conditional Survival Models in modelling ambulance response times

Many of the challenges faced in health care delivery can be informed through building models. In particular, Discrete Conditional Survival (DCS) models, recently under development, can provide policymakers with a flexible tool to assess time-to-event data. The DCS model is capable of modelling the survival curve based on various underlying distribution types and is capable of clustering or grouping observations (based on other covariate information) external to the distribution fits. The flexibility of the model comes through the choice of data mining techniques that are available in ascertaining the different subsets and also in the choice of distribution types available in modelling these informed subsets. This paper presents an illustrated example of the Discrete Conditional Survival model being deployed to represent ambulance response-times by a fully parameterised model. This model is contrasted against use of a parametric accelerated failure-time model, illustrating the strength and usefulness of Discrete Conditional Survival models.