Building an Academic Tradition: Durham University and the Development of British Oriental Studies in the Post-War Era

In 1947, a Foreign Office report chaired by Lord Scarbrough claimed that British universities should ‘build an academic tradition’ in Oriental Studies akin to that which existed in the traditional humanities. This thesis analyses the limitations to this ambition through an historical study of the well-reputed School of Oriental Studies (1950-1989) and its successor, the Department of East Asian Studies (1989-2007), both of which existed at Durham University. It considers the underlying forces which led to the establishment and closure of these academic departments within the wider context of UK government policies and university politics. Close attention is also paid to the significance of specific individuals who contributed to the growth or closure of these departments at Durham. The purpose of this very specific case study is to engage – via recent historical example – with the challenges facing Oriental Studies as a valid academic subject and its continuing relevance in British Universities. It therefore includes an introductory discussion about the nature of Oriental Studies as a subject, the meaning of the term ‘the Orient’, and a brief history of Oriental scholarship in Britain since medieval times

A significant problem with using the Amati relation for cosmological purposes

We consider the distribution of many samples of gamma-ray bursts when plotted in a diagram with their bolometric fluence (Sbolo) versus the observed photon energy of peak spectral flux (E peak, obs). In this diagram, all bursts that obey the Amati relation (a luminosity relation where the total burst energy has a power-law relation to E peak, obs) must lie above some limiting line, although observational scatter is expected to be substantial. We confirm that early bursts with spectroscopic redshifts are consistent with this Amati limit. But we find that the bursts from BATSE, Swift, Suzaku, and Konus are all greatly in violation of the Amati limit, and this is true whether or not the bursts have measured spectroscopic redshifts. That is, the Amati relation has definitely failed. In the S bolo-E peak, obs diagram, wefind that every satellite has a greatly different distribution. This requires that selection effects are dominating these distributions, which we quantitatively identify. For detector selections, the trigger threshold and the threshold for the burst to obtain a measured E peak, obs combine to make a diagonal cutoff with the position of this cutoff varying greatly detector to detector. For selection effects due to the intrinsic properties of the burst population, the distribution of E peak, obs makes bursts with low and high values rare, while the fluence distribution makes bright bursts relatively uncommon. For a detector with a high threshold, the combination of these selection effects serves to allow only bursts within a region along the Amati limit line to be measured, and these bursts will then appear to follow an Amati relation. Therefore, the Amati relation is an artifact of selection effects within the burst population and the detector. As such, the Amati relation should not be used for cosmological tasks. This failure of the Amati relation is in no way prejudicial against the other luminosity relations. © 2012. The American Astronomical Society. All rights reserved

The development of the adult deterioration detection system (ADDS) chart

The Adult Deterioration Detection System (ADDS) observation chart described in this short report was developed as part of a research project carried out at The University of Queensland for Queensland Health and the Australian Commission on Safety and Quality in Health Care (ACSQHC). The aim of the project was to investigate the design and use of observation charts in recognising and managing patient deterioration, including the design and evaluation of a new adult observation chart that incorporated human factors principles

Paper-based patient chart design information sheet

The purpose of this document is to help those involved in creating paper-based patient charts improve the human factors aspects of the design of their charts. It is based on the outcomes of a research project (“Human Factors Research Regarding Observation Charts”) carried out at the University of Queensland for the Australian Commission on Safety and Quality in Health Care, the Queensland Health Patient Safety and Quality Improvement Service and the Clinical Skills Development Service. Copies of the reports associated with this project are available online from the Commission’s website (www.safetyandquality.gov.au). As part of this project, we systematically reviewed 25 existing patient observation charts and developed a new chart (the “ADDS chart”) designed to identify patient deterioration, which was then evaluated in behavioural experiments. In this document, we will use some of the issues arising from this process to illustrate human factors design considerations for paper-based patient charts in general

Detecting abnormal vital signs on six observation charts: An experimental comparison

Paper-based observation charts are the principal means of monitoring changes to patients’ vital signs. There is considerable variation in the design of observation charts and a lack of empirical research on the performance of different designs. This report describes the results of a study carried out as part of a project funded by the Australian Commission for Safety and Quality in Health Care and Queensland Health to investigate the design and use of observation charts in recognising and managing patient deterioration, including the design and evaluation of a new adult observation chart that incorporated human factors principles. The first phase of this project involved using a procedure known as heuristic analysis to review 25 observation charts from Australia and New Zealand. 1,189 usability problems, which could lead to errors in recording data and identifying patient deterioration, were identified in the charts. The results from the heuristic analysis were used to design a new chart (the Adult Deterioration Detection System [ADDS] chart) based on human factors principles and current best practice. The study described in this report involved an empirical comparison of six charts (two versions of the ADDS chart, two existing charts rated as “well designed” in the heuristic analysis, one existing chart rated as being of “average design”, and one existing chart rated as “poorly designed”). Novices (individuals who were unfamiliar with using patient charts) and health professionals (doctors and nurses) were recruited as participants. Each chart design was shown to each participant four times displaying different physiological data with one abnormal vital sign (e.g. a high systolic blood pressure), and four times displaying different normal physiological data. After memorising the normal ranges for each vital sign, participants had to classify the physiological data on the charts as “normal” or “abnormal”. Error rates (the proportion of trials where participants made an incorrect normal/abnormal judgement) and response time (the time to read the chart and make the judgement) were measured. Results indicated that chart design had a statistically significant effect on both error rates and response time, with the charts identified as having better design tending to yield fewer errors and shorter decision times. Specifically, the two versions of the ADDS chart outperformed all the existing charts on both metrics, where the other charts yielded between 2.5 and 3.3 times as many errors as the ADDS chart. There was no significant difference between novices and health professionals in error rates for any chart, but the health professionals were significantly faster than novices at making their decisions for the charts rated as “average” and “poor”. There was no significant difference between doctors and nurses on either of the two performance measures for any of the charts. These data indicate that differences in the design of observation charts have a profound impact on chart users’ decisions regarding patients’ vital signs as well as the time it takes to make such decisions. Based on the current data, it appears that the ADDS chart is significantly better at signalling patient deterioration than other currently available charts

An Online Survey of Health Professionals’ Opinions Regarding Observation Charts

The current study was the second stage of a project funded by the Australian Commission for Quality and Safety in Health Care and Queensland Health to investigate the design and use of observation charts in recognising and managing patient deterioration, including the design and evaluation of a new adult observation chart that incorporated human factors principles. Improving the recognition and management of patients who deteriorate whilst in hospital is a frequently cited goal for patient safety. Changes in physiological observations or ‘vital signs’ commonly precede serious adverse medical events. Paper-based observation charts are the chief means of recording and monitoring changes to patients’ vital signs. One approach to improve the recognition and management of deteriorating patients is to improve the design of paper-based observation charts (note that the management of patient deterioration can potentially be affected by chart design if, for example, action plans are included on the chart). There is considerable variation in the design of observation charts in current use in Australia and a lack of empirical research on the performance of observation charts in general. The aim of the current study was to gauge the opinions of the population who actually use observation charts. We recruited a large sample of health professionals (N = 333) to answer general questions about the design of observation charts and specific questions about nine observation charts. The participants reported using observation charts daily, but only a minority reported having received any formal training in the use of such charts. In our previously-reported heuristic analysis of observation charts (1), we found that the majority of charts included a large number of abbreviations. In this survey, participants were asked to nominate which term they first thought of when seeing a particular abbreviation. Most abbreviations were overwhelmingly assigned the same meaning. However, some abbreviations had groups of participants nominating different terms for the same abbreviation. Participants were also asked to nominate their preferred terms for nine vital signs that commonly appear on observation charts. For some vital signs, there was a high level of agreement as to which term was easiest to understand; however, for other vital signs, there was no clearly preferred term. Participants were also asked about their chart design preferences both in terms of (a) recording observations and (b) detecting deterioration. In both instances, participants preferred the option to “Plot the value on a graph with graded colouring, where the colours correspond to a scoring system or graded responses for abnormality”. Participants’ preference was in line with what a human factors approach would recommend (i.e. charts with a colour-coded track and trigger system). In the final sections of the survey, participants were first asked to respond to 13 statements regarding the design of their own institution’s current observation chart, and then to respond to the same 13 statements for one of nine randomly-assigned observation charts. The nine observation charts included the new Adult Deterioration Detection System (ADDS) chart and eight charts of “good”, “average”, or “poor” design quality from the heuristic analysis. Participants’ mean aggregated rating across the 13 items for their institution’s current observation chart was close to the scale’s mid-point, 3 = neutral. For the assigned charts, there was a statistically significant effect of chart type on the aggregated rating. The a priori “poor” quality charts were each rated as having a significantly poorer design compared with each of the other charts (collectively, the a priori “average” and “good” quality charts). There was partial support for our hypothesis that health professionals would rate the “good” charts as having better design, compared to the “average” and “poor” charts. In conclusion, the online survey served two main purposes. First, it collected quantitative data on health professionals’ general preferences regarding aspects of the design of observation charts. This information informed the design of the ADDS chart and could also be used by other chart designers to produce more user-friendly hospital charts. Second, the online survey enabled health professionals to rate the design of the new ADDS chart as well as eight existing charts of varying quality. Overall, health professionals agreed with our human factors-based rating with regards to the “poor” quality charts. However, the health professionals did not differentiate between the “average” and “good” quality charts in their ratings

Connecting our world

This workshop will give participants the opportunity to build physical interfaces using a wide range of materials and calibrate them to enhance the existing capabilities of the Scratch Picoboard