Software psychology and the computerisation of the weighted application blank : a thesis presented in partial fulfillment of the requirements for the degree of Master of Arts in Psychology at Massey University

This study investigated the use of a Weighted Application Blank (WAB) for selecting candidates likely to pass the first year of a comprehensive nursing course. A subject pool of 415 comprehensive nursing course applicants was drawn from 1980 to 1985 first year Polytechnic classes. A discriminant analysis on the application form responses made by these subjects was performed. Computer software was then developed incorporating results from Human Factors research. The software aimed to computerise the WAB method of classifying applicants following principles of software psychology. A group of 50 computer naive subjects participated in an experimental evaluation of the software. Five subjects took part in initial pilot study trials of the software. The remaining 45 subjects' were divided into three equally sized groups. The subjects task was to enter eight sets of nursing course application form data. The "computerised" group received instructions on how to do this from the screen, the "written" group from a manual and the "verbal" group verbally from the experimenter. Time taken to complete the task and the number of errors made were recorded. Three ANOVAs were performed to establish if group exerted an influence on trial times or error rates. In addition, applicants were required to complete two questionnaires. The first prior to the experimental trials and the second following them. Results indicated that group influenced time taken on the task (F(1,294) = 7.43, p<.001). Group did not exert an influence on errors made on each question (F(32,672) = 1.022, p>.05). The interaction between errors made on each application form and group was significant (F(14,294) = 2.809,p.05). Responses to the questionnaires were evaluated and an assessment was made of the responses. It was concluded that the fields of human­ computer interface design and personnel selection had been successfully combined. Leading to the expectation that an area of great research potential had been opened up

"Selection of Input Parameters for Multivariate Classifiersin Proactive Machine Health Monitoring by Clustering Envelope Spectrum Harmonics"

In condition monitoring (CM) signal analysis the inherent problem of key characteristics being masked by noise can be addressed by analysis of the signal envelope. Envelope analysis of vibration signals is effective in extracting useful information for diagnosing different faults. However, the number of envelope features is generally too large to be effectively incorporated in system models. In this paper a novel method of extracting the pertinent information from such signals based on multivariate statistical techniques is developed which substantialy reduces the number of input parameters required for data classification models. This was achieved by clustering possible model variables into a number of homogeneous groups to assertain levels of interdependency. Representatives from each of the groups were selected for their power to discriminate between the categorical classes. The techniques established were applied to a reciprocating compressor rig wherein the target was identifying machine states with respect to operational health through comparison of signal outputs for healthy and faulty systems. The technique allowed near perfect fault classification. In addition methods for identifying seperable classes are investigated through profiling techniques, illustrated using Andrew’s Fourier curves

‘Riding the waves’ - an exploration of how students undertaking a pre-registration nursing programme develop emotional resilience

Background The study was prompted by recognition of the many emotional demands and challenges on mature students undertaking professional qualifying programmes. These can cause excessive levels of stress and anxiety with an impact on retention of students on programmes. Aim and objectives The overall aim was to identify what pre-registration nurses identified as challenges or adversity in their transition from health care support workers to accountable professionals and what factors they perceived as significant in contributing to their own emotional resilience. The objective was then to make specific recommendations related to the nursing curriculum, academic and work based support structures in order to promote resilience. Participants Participants were pre-registration nursing students on adult and mental health branches nearing the end of their final year of a pre-registration nursing programme with the Open University. Methods A qualitative methodology was used with use of one focus group and eleven in depth interviews. Results Four different dimensions of resilience were identified; ways of being/personal characteristics, personal survival tactics, immediate social and work based environment and wider social and cultural environment. Key findings included the importance of peer support, positive feedback and enhancing the student’s ability to re-frame difficulties or problems, a positive culture of work place learning , supporting and validating personal reflection outside academic discourses, and support in ‘meaning making’. As well as peer support, examples of good practice demonstrated by mentors, programme tutors and tutors were essential in supporting students in these identified areas. Conclusion and recommendations Emotional resilience is a multi-dimensional concept and different levels of intervention are therefore needed to promote it. The curriculum needs to reflect the importance of affective as well as cognitive aspects of development in order to promote the resilience of students and support structures need to be embedded in programme design to promote peer interaction and sharing of good practice between those in education roles. Key words Pre-registration nursing education, emotional resilience, adversity, communities of practic

The reported expression of pain and distress by people with an intellectual disability

Aims and objectives.  To explore the assumption that people with ID are unable to communicate effectively about pain by examining the extent to which they were reported as using language and behaviour that was readily understandable to others to communicate pain as distinct from distress. Background.  The healthcare needs of people with an intellectual disability (ID) are frequently overlooked or dealt with inappropriately. One proposed reason is the difficulty that such individuals have in communicating about their pain. Design.  A postal questionnaire-based mixed method design was used. Methods.  Data from carer reports (n = 29) of the ways people with ID supported expressed pain and distress were categorised and analysed using descriptive statistics and thematic content analysis. Results.  Nineteen of the 22 people who used verbal communication were reported to express pain using words that would be understandable to someone else, often accompanied by behavioural indications of the location of the pain. The language and behaviour that were reported as being used to express distress was more idiosyncratic, and there was little overlap between this and the ways in which pain was expressed. Conclusion.  The results provide some challenges to the view that people with ID are necessarily unable to communicate effectively about their pain and support the view that pain and distress can be conceptually distinguished and differentially communicated by some people with ID. Relevance to clinical practice.  The results suggest that many people with ID can be active participants in describing their experience of pain and that nurses should attempt to obtain this information directly from the individual during the diagnostic process. Nurses should be mindful of the distinction between pain and distress and should not respond to signs of distress in this group as being indicative of pain, without carrying out further assessment

Selection of Input Parameters for Multivariate Classifiers in Proactive Machine Health Monitoring by Clustering Envelope Spectrum Harmonics

In condition monitoring (CM) signal analysis the inherent problem of key characteristics being masked by noise can be addressed by analysis of the signal envelope. Envelope analysis of vibration signals is effective in extracting useful information for diagnosing different faults. However, the number of envelope features is generally too large to be effectively incorporated in system models. In this paper a novel method of extracting the pertinent information from such signals based on multivariate statistical techniques is developed which substantialy reduces the number of input parameters required for data classification models. This was achieved by clustering possible model variables into a number of homogeneous groups to assertain levels of interdependency. Representatives from each of the groups were selected for their power to discriminate between the categorical classes. The techniques established were applied to a reciprocating compressor rig wherein the target was identifying machine states with respect to operational health through comparison of signal outputs for healthy and faulty systems. The technique allowed near perfect fault classification. In addition methods for identifying seperable classes are investigated through profiling techniques, illustrated using Andrew’s Fourier curves