Identification and doing without it, V: A formal mathematical analysis for a case of mix-up of individuals, and of recovery from failure to attain identification

Identification, when sought, is not necessarily obtained. Operational guidance that is normatively acceptable may be necessary for such cases. We proceed to formalize and illustrate modes of exchanges of individual identity, and provide procedures of recovery strategies in specific prescriptions from an ancient body of law for such situations when, for given types of purposes, individuals of some relevant kind had become intermixed and were undistinguishable. Rules were devised, in a variety of domains, for coping with situations that occur if and when the goal of identification was frustrated. We propose or discuss mathematical representations of such recovery procedures.

Workplace violence and intentions to quit: results from a survey of London nurses

Study Objective: Work-place violence, harassment and abuse is an increasing feature of nurses’ experience of work in many countries. There is some evidence that the experience of workplace violence affects levels of job satisfaction (Hesketh et al 2003) and career decisions (e.g. Mayer et al 1999, Fernandes et al 1999). This paper reports on verbal and physical abuse by patients, relatives and carers, as well as racial and sexual harassment in Acute Hospitals in London and investigates whether workplace violence affects nurses’ intentions to leave either their current job or the nursing profession, controlling for a number of other factors that are known to affect career decisions, such as workload, pay and own health. Method: A questionnaire designed by two of the authors (Reeves and West) to assess many different aspects of nurses work life was used in a postal survey of nurses grades A to I practising in twenty London acute trusts in 2002. A total of 6,160 clinical nurses were mailed the questionnaires and 2,880 returned completed questionnaires, resulting in an overall response rate of 47%, discounting undelivered questionnaires. Respondents worked in a wide variety of clinical settings but mainly in acute medical and surgical wards. In addition to descriptive statistics, results were analysed using logistic regression with robust standard errors: the appropriate test when the dependent variable is dichotomous and the individual respondents clustered within units (nurses working within hospitals are not statistically independent). Results: Our results show high levels of racial (%), sexual (%) and other, unspecified forms of harassment (%), as well as verbal and physical abuse (14% had been physically assaulted with 5% being assaulted more than once), over the previous 6 months. A very small number (1%) reported experiencing all three forms of harassment; 12% two forms and 29% one form. Only 45% of this sample intended to stay in nursing for at least 3 years; 40% were undecided and 15% intended to leave. Logistic regression estimates showed that reported levels of abuse and harassment had a significant impact on respondents’ career intentions, even in models that controlled for known factors affecting career decisions. About 70% of our respondents reported that they had had too little training in dealing with aggressive behaviour—or none at all—but there was no statistical relationship between lack of training and reported assaults. Conclusions: The international shortage of health care workers is due at least in part to low retention rates. It is crucial to investigate nurses’ experiences of work to identify the factors that shape their career decisions. Workplace violence is increasingly acknowledged as an international, service-wide, health care problem. This paper adds to the literature that shows that workplace violence has an impact on nurses’ career decisions. The implications for managers and policy makers are that strengthening systems of security and providing nurses with training in interpersonal relationships including dealing with aggressive patients could slow nurse turnover.

Support vector machines for regression and applications to software quality prediction

Software metrics are the key tool in software quality management. In this paper, we propose to use support vector machines for regression applied to software metrics to predict software quality. In experiments we compare this method with other regression techniques such as Multivariate Linear Regression, Conjunctive Rule and Locally Weighted Regression. Results on benchmark dataset MIS, using mean absolute error, and correlation coefficient as regression performance measures, indicate that support vector machines regression is a promising technique for software quality prediction. In addition, our investigation of PCA based metrics extraction shows that using the first few Principal Components (PC) we can still get relatively good performance.