Geometric shape errors in forging: developing a metric and an inverse model

The complexity of the forging process ensures that there is inherent variability in the geometric shape of a forged part. While knowledge of shape error, comparing the desired versus the measured shape, is significant in measuring part quality the question of more interest is what can this error suggest about the forging process set-up? The first contribution of this paper is to develop a shape error metric which identifies geometric shape differences that occur from a desired forged part. This metric is based on the point distribution deformable model developed in pattern recognition research. The second contribution of this paper is to propose an inverse model that identifies changes in process set-up parameter values by analysing the proposed shape error metric. The metric and inverse models are developed using two sets of simulated hot-forged parts created using two different die pairs (simple and 'M'-shaped die pairs). A neural network is used to classify the shape data into three arbitrarily chosen levels for each parameter and it is accurate to at least 77 per cent in the worst case for the simple die pair data and has an average accuracy of approximately 80 per cent when classifying the more complex 'M'-shaped die pair data.

Integrating errors into developmental assessment: 'time' for ages 8-13

It is widely agreed that measurement is of paramount importance to students’ overall development in mathematics. This paper describes a developmental ‘map’ of students’ understanding and skills in measurement, focussed on the topic of Time, that integrates correct and incorrect student ideas. The map is based on a Rasch analysis of data from a large-scale UK national survey for standardising assessment for children from 5 to 14 years of age. It is demonstrated how a partial credit strategy enables a developmental map to be constructed to show students’ strengths and weaknesses in a meaningful and useful summative and formative manner. This map provides evidence, of both a summative and a formative nature, which may enable teachers to craft appropriate and successful learning experiences for children.

The ethics and practical importance of defining, distinguishing and disclosing nursing errors : a discussion paper

Nurses globally are required and expected to report nursing errors. As is clearly demonstrated in the international literature, fulfilling this requirement is not, however, without risks. In this discussion paper, the notion of ‘nursing error’, the practical and moral importance of defining, distinguishing and disclosing nursing errors and how a distinct definition of ‘nursing error’ fits with the new ‘system approach’ to human-error management in health care are critiqued. Drawing on international literature and two key case exemplars from the USA and Australia, arguments are advanced to support the view that although it is ‘right’ for nurses to report nursing errors, it will be very difficult for them to do so unless a non-punitive approach to nursing-error management is adopted.

Analysis of children's merging errors

Array

Optimising errors in signaling corporate collapse using MCCCRA

Purpose – The purpose of this paper is to put forward an innovative approach for reducing the variation between Type I and Type II errors in the context of ratio-based modeling of corporate collapse, without compromising the accuracy of the predictive model. Its contribution to the literature lies in resolving the problematic trade-off between predictive accuracy and variations between the two types of errors.

Design/methodology/approach – The methodological approach in this paper – called MCCCRA – utilizes a novel multi-classification matrix based on a combination of correlation and regression analysis, with the former being subject to optimisation criteria. In order to ascertain its accuracy in signaling collapse, MCCCRA is empirically tested against multiple discriminant analysis (MDA).

Findings – Based on a data sample of 899 US publicly listed companies, the empirical results indicate that in addition to a high level of accuracy in signaling collapse, MCCCRA generates lower variability between Type I and Type II errors when compared to MDA.

Originality/value – Although correlation and regression analysis are long-standing statistical tools, the optimisation constraints that are applied to the correlations are unique. Moreover, the multi-classification matrix is a first in signaling collapse. By providing economic insight into more stable financial modeling, these innovations make an original contribution to the literature.

Reported medication errors after introducing an electronic medication management system

Aims and objectives
To explore the effects of introducing an electronic medication management system on reported medication errors.
Background
Computerised medication management systems have been found to improve medication safety; however, introducing medication management system into healthcare environments can create unanticipated or new problems and opportunities for medication error.
Design
Descriptive analysis of medication error reports.
Methods
This was a retrospective analysis of 359 incident reports drawn from the period of 1 May 2005–30 April 2006 across two hospital sites of a single not-for-profit private health service located in metropolitan Melbourne. Site A used a conventional pen and paper system for medication management, and Site B had introduced a computerised medication management system.
Results
Most medication errors occurred at the nurse administration (71·5%) and prescribing (16·4%) stages of delivery. The most common medication error type reported at Site A was omission (33%), and at Site B was wrong documentation (24·2%). A higher proportion of errors at the prescribing phase, and less nurse administration errors, were detected at Site B where the medication management system was in use. The incidence of other, less frequent errors was similar across the two hospital sites.
Conclusions
This examination of medication error reports suggests there are differences in the types of medication errors that are reported in association with the introduction of electronic medication management system compared to pen and paper system systems. The findings provide a new insight into the effects of introducing an electronic medication management system on the types of medication errors reported.
Relevance to clinical practice
The findings provide a new insight into the types of medication errors that are reported during implementation of an electronic medication management system. Extra support for physicians prescribing practices should be considered.

Recruitment into diabetes prevention programs : what is the impact of errors in self-reported measures of obesity?

Background
Error in self-reported measures of obesity has been frequently described, but the effect of self-reported error on recruitment into diabetes prevention programs is not well established. The aim of this study was to examine the effect of using self-reported obesity data from the Finnish diabetes risk score (FINDRISC) on recruitment into the Greater Green Triangle Diabetes Prevention Project (GGT DPP).

Methods
The GGT DPP was a structured group-based lifestyle modification program delivered in primary health care settings in South-Eastern Australia. Between 2004–05, 850 FINDRISC forms were collected during recruitment for the GGT DPP. Eligible individuals, at moderate to high risk of developing diabetes, were invited to undertake baseline tests, including anthropometric measurements performed by specially trained nurses. In addition to errors in calculating total risk scores, accuracy of self-reported data (height, weight, waist circumference (WC) and Body Mass Index (BMI)) from FINDRISCs was compared with baseline data, with impact on participation eligibility presented.

Results
Overall, calculation errors impacted on eligibility in 18 cases (2.1%). Of n = 279 GGT DPP participants with measured data, errors (total score calculation, BMI or WC) in self-report were found in n = 90 (32.3%). These errors were equally likely to result in under- or over-reported risk. Under-reporting was more common in those reporting lower risk scores (Spearman-rho = −0.226, p-value < 0.001). However, underestimation resulted in only 6% of individuals at high risk of diabetes being incorrectly categorised as moderate or low risk of diabetes.

Conclusions
Overall FINDRISC was found to be an effective tool to screen and recruit participants at moderate to high risk of diabetes, accurately categorising levels of overweight and obesity using self-report data. The results could be generalisable to other diabetes prevention programs using screening tools which include self-reported levels of obesity.