The aims of this study were: (1) to evaluate two different therapeutic approaches, namely direct versus indirect suggestions, in reducing pain during lumbar punctures; and (2) to explore the relationship between hypnotizability and outcome. Thirty children with leukaemia and non‐Hodgkin's lymphoma who were undergoing regular lumbar punctures were randomly allocated to two groups. In one group, children were hypnotized and given direct suggestions associated with pain relief whilst undergoing lumbar puncture. In the second group children were given indirect hypnotic suggestions associated with pain relief. After hypnotic intervention, there was a statistically significant reduction over baseline for pain and anxiety during lumbar punctures in both groups. Direct and indirect methods were found to be equally effective. The level of hypnotizability was significantly associated with the magnitude of treatment outcome. Copyright © 1998 British Society of Experimental and Clinical Hypnosis

No abstract

The paper sets out the challenges facing the Police in respect of the detection and prevention of the volume crime of burglary. A discussion of data mining and decision support technologies that have the potential to address these issues is undertaken and illustrated with reference the authors' work with three Police Services. The focus is upon the use of ''soft'' forensic evidence which refers to modus operandi and the temporal and geographical features of the crime, rather than ''hard'' evidence such as DNA or fingerprint evidence. Three objectives underpin this paper. First, given the continuing expansion of forensic computing and its role in the emergent discipline of Crime Science, it is timely to present a review of existing methodologies and research. Second, it is important to extract some practical lessons concerning the application of computer science within this forensic domain. Finally, from the lessons to date, a set of conclusions will be advanced, including the need for multidisciplinary input to guide further developments in the design of such systems. The objectives are achieved by first considering the task performed by the intended systems users. The discussion proceeds by identifying the portions of these tasks for which automation would be both beneficial and feasible. The knowledge discovery from databases process is then described, starting with an examination of the data that police collect and the reasons for storing it. The discussion progresses to the development of crime matching and predictive knowledge which are operationalised in decision support software. The paper concludes by arguing that computer science technologies which can support criminal investigations are wide ranging and include geographical information systems displays, clustering and link analysis algorithms and the more complex use of data mining technology for profiling crimes or offenders and matching and predicting crimes. We also argue that knowledge from disciplines such as forensic psychology, criminology and statistics are essential to the efficient design of operationally valid systems.

An essential component of criminal investigation involves the interrogation of large databases of information held by police and other criminal justice agencies. Data mining and decision support systems have an important role to play in assisting human inference in this forensic domain that creates one of the most challenging decision-making environments. Technologies range widely and include social network analysis, geographical information systems, and data mining technologies for clustering crimes, finding links between crime and profiling offenders, identifying criminal networks, matching crimes, generating suspects, and predicting criminal activity. This paper does not intend to cover the gamut of techniques available to the investigator of crime as this has been presented elsewhere (Oatley GC, Ewart BW, Zeleznikow J. Decision support systems for police: lessons from the application of data mining techniques to 'soft' forensic evidence. Artif Intell Law 2006, 14:35-100). Rather, the objective is to highlight issues of implementation and interpretation of the techniques available to the crime analyst. To this end, the authors draw from their experiences of working with real-world crime databases (Oatley GC, Belem B, Fernandes K, Hoggarth E, Holland B, Lewis C, Meier P, Morgan K, Santhanam J, et al. The gang gun-crime problem-solutions from social network theory, epidemiology, cellular automata, Bayesian networks and spatial statistics. Accepted: book chapter for IEEE publication Computational Forensics; 2008; Oatley GC, McGarry K, Ewart BW. Offender network metrics. WSEAS Trans Inf Sci Appl 2006, 3:2440-2448; Oatley GC, Ewart BW. Crimes analysis software: pins in maps, clustering and Bayes net prediction. Expert Syst Appl 2003, 25:569-588), involving gun and gang crime, fraud, terrorism, burglary, and retail crime.

Brian Ewart lectures in forensic psychology at the University of Sunderland. Formerly with the Home Office Prison Psychological Services, he specialises in the application of psychology within the forensic domain and has published on judicial decision-making, victimisation and predictive systems for burglary. Giles Oatley and Kevin Burn are computer scientists; Giles specialises in the development of software for forensic applications, while Kevin is a specialist in the application of fuzzy logic to real-world solutions.ABSTRACT 'Hard' forensic evidence (eg DNA) may be the best means of linking crimes, but it is often absent at burglary crime scenes. Modus operandi information is always present to some degree, but little is known of its significance in matching burglaries. This paper evaluates the ability of three algorithms to match a target crime to the actual offender within a database of 966 offences. The first (RCPA) uses only MO information, the second (RPAL) only temporal and geographic data and a third (COMBIN) is a combination of the two. A score of one indicates a perfect match between the target crime and the case selected by the algorithm. The lowest possible rank is 965showing that 965 cases were selected before the target offence. The RPAL and COMBIN each achieve a perfect match for 24 per cent of the crimes and succeed in matching over half of the crimes at a score of 10 or less. For prolific offenders, using MO information alone is better than temporal and geographic data, although the best performance is achieved when in combination. Behavioural, spatial and temporal information is collected by many Police Services. The value and means of utilising such data in linking crimes is clearly demonstrated.