Parental information needs in chronic renal failure and diabetes mellitus

The information needs of parents of children with end stage renal failure (ESRF) or with insulin dependent diabetes mellitus (IDDM) were assessed by questionnaires over a 2-year period. Questionnaires were posted on seven occasions at 4-monthly intervals and were sent to both mothers and fathers. Most information needs were reported to be for detailed test results, for new information about the condition and about the child's future social development. Questions responsible for the three highest scores were concerned with the future: the child's fertility; their social, career and marriage prospects; and the hope for a new improved treatment. For the IDDM mothers, scores were significantly different depending on age of the child (P = 0.02). Change in treatment mode had no significant effect on the information needs of parents of children with ESRF (P = 0.81). Occupation was significantly associated with the mean general information needs scores for parents, with occupations of a lower socioeconomic status associated with higher information needs scores. There were no significant differences between the reported mean general information needs scores of parents of children with ESRF and of parents of children with IDDM (P = 0.69) or between mothers and fathers mean general information needs scores (P = 0.58). CONCLUSION: Multidisciplinary team members need to tailor information to the needs of the individual families and be sensitive to socioeconomic factors and communication issues.

Knowledge management systems:finding a way with technology

Purpose - To consider the role of technology in knowledge management in organizations, both actual and desired. Design/methodology/approach - Facilitated, computer-supported group workshops were conducted with 78 people from ten different organizations. The objective of each workshop was to review the current state of knowledge management in that organization and develop an action plan for the future. Findings - Only three organizations had adopted a strongly technology-based "solution" to knowledge management problems, and these followed three substantially different routes. There was a clear emphasis on the use of general information technology tools to support knowledge management activities, rather than the use of tools specific to knowledge management. Research limitations/implications - Further research is needed to help organizations make best use of generally available software such as intranets and e-mail for knowledge management. Many issues, especially human, relate to the implementation of any technology. Participation was restricted to organizations that wished to produce an action plan for knowledge management. The findings may therefore represent only "average" organizations, not the very best practice. Practical implications - Each organization must resolve four tensions: Between the quantity and quality of information/knowledge, between centralized and decentralized organization, between head office and organizational knowledge, and between "push" and "pull" processes. Originality/value - Although it is the group rather than an individual that determines what counts as knowledge, hardly any previous studies of knowledge management have collected data in a group context.

Drug information in general practice pharmacy

The drug information sources currently available to general practice pharmacists have been identified. The use of and attitudes to these sources were assessed as well as the perceived information needs of practising pharmacists. The special requirements of women pharmacists and pharmacists working part-time were studied. The relationship of the medical representative as an information source for pharmacists was evaluated. Participation in continuing education programmes as a vital means of ensuring current information awareness and knowledge for the practising profession has been considered. Investigations were mainly pursued by questionnaire survey, while computer facilities were used for the processing and the analyses of data. The desirability of collated and evaluated information from one or more independent authoritative sources has been discussed. The increasing advisory role of the general practice pharmacist and the needs of the patient and potential customer have been discussed, with projections for the pharmacist's future health care contribution.

Information geometric measurements of generalisation

Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. Generalisation is measured by the performance on independent test data drawn from the same distribution as the training data. Such performance can be quantified by the posterior average of the information divergence between the true and the model distributions. Averaging over the Bayesian posterior guarantees internal coherence; Using information divergence guarantees invariance with respect to representation. The theory generalises the least mean squares theory for linear Gaussian models to general problems of statistical estimation. The main results are: (1)~the ideal optimal estimate is always given by average over the posterior; (2)~the optimal estimate within a computational model is given by the projection of the ideal estimate to the model. This incidentally shows some currently popular methods dealing with hyperpriors are in general unnecessary and misleading. The extension of information divergence to positive normalisable measures reveals a remarkable relation between the dlt dual affine geometry of statistical manifolds and the geometry of the dual pair of Banach spaces Ld and Ldd. It therefore offers conceptual simplification to information geometry. The general conclusion on the issue of evaluating neural network learning rules and other statistical inference methods is that such evaluations are only meaningful under three assumptions: The prior P(p), describing the environment of all the problems; the divergence Dd, specifying the requirement of the task; and the model Q, specifying available computing resources.

General gaussian priors for improved generalisation

We explore the dependence of performance measures, such as the generalization error and generalization consistency, on the structure and the parameterization of the prior on `rules', instanced here by the noisy linear perceptron. Using a statistical mechanics framework, we show how one may assign values to the parameters of a model for a `rule' on the basis of data instancing the rule. Information about the data, such as input distribution, noise distribution and other `rule' characteristics may be embedded in the form of general gaussian priors for improving net performance. We examine explicitly two types of general gaussian priors which are useful in some simple cases. We calculate the optimal values for the parameters of these priors and show their effect in modifying the most probable, MAP, values for the rules.