The struggle for control over the voices of the past and the socializing role of precolonial history: perspectives on the production of precolonial educative materials

Paper presented at the Wits History Workshop: The Making of Class, 9-14 February, 198

An investigation of machine learning based prediction systems

Traditionally, researchers have used either o�f-the-shelf models such as COCOMO, or developed local models using statistical techniques such as stepwise regression, to obtain software eff�ort estimates. More recently, attention has turned to a variety of machine learning methods such as artifcial neural networks (ANNs), case-based reasoning (CBR) and rule induction (RI). This paper outlines some comparative research into the use of these three machine learning methods to build software e�ort prediction systems. We briefly describe each method and then apply the techniques to a dataset of 81 software projects derived from a Canadian software house in the late 1980s. We compare the prediction systems in terms of three factors: accuracy, explanatory value and configurability. We show that ANN methods have superior accuracy and that RI methods are least accurate. However, this view is somewhat counteracted by problems with explanatory value and configurability. For example, we found that considerable eff�ort was required to configure the ANN and that this compared very unfavourably with the other techniques, particularly CBR and least squares regression (LSR). We suggest that further work be carried out, both to further explore interaction between the enduser and the prediction system, and also to facilitate configuration, particularly of ANNs

The Data Life Aquatic

   This paper assesses data consumers’ perspectives on the interoperable and re-usable aspects of the FAIR Data Principles. Taking a domain-specific informatics approach, ten oceanographers were asked to think of a recent search for data and describe their process of discovery, evaluation, and use. The interview schedule, derived from the FAIR Data Principles, included questions about the interoperability and re-usability of data. Through this critical incident technique, findings on data interoperability and re-usability give data curators valuable insights into how real-world users access, evaluate, and use data. Results from this study show that oceanographers utilize tools that make re-use simple, with interoperability seamless within the systems used. The processes employed by oceanographers present a good baseline for other domains adopting the FAIR Data Principles.&nbsp

Gender Equity in Transplantation: A Report From the Women in Transplantation Workshop of The Transplantation Society of Australia and New Zealand

The exponential growth of young talented women choosing science and medicine as their professional career over the past decade is substantial. Currently, more than half of the Australian medical doctoral graduates and early career researchers are comprised of women, but less than 20% of all academic professorial staff are women. The loss of female talent in the hierarchical ladder of Australian academia is a considerable waste of government investment, productivity, and scientific innovation. Gender disparity in the professional workforce composition is even more striking within the field of transplantation. Women are grossly underrepresented in leadership roles, with currently no female heads of unit in any of the Australian and New Zealand transplanting centers. At the same time, there is also gender segregation with a greater concentration of women in lower-status academic position compared with their male counterparts. Given the extent and magnitude of the disparity, the Women in Transplantation Committee, a subcommittee of The Transplantation Society of Australia and New Zealand established a workshop comprising 8 female clinicians/scientists in transplantation. The key objectives were to (i) identify potential gender equity issues within the transplantation workforce; (ii) devise and implement potential strategies and interventions to address some of these challenges at a societal level; (iii) set realistic and achievable goals to enhance and facility gender equality, equity, and diversity in transplantation

Prevalence, treatment and correlates of depression in multiple sclerosis

BackgroundThe prevalence of depression in Multiple Sclerosis (MS) is often assessed by administering patient reported outcome measures (PROMs) examining depressive symptomatology to population cohorts; a recent review summarised 12 such studies, eight of which used the Hospital Anxiety and Depression Scale-Depression (HADS-D). In clinical practice, depression is diagnosed by an individual structured clinical interview; diagnosis often leads to treatment options including antidepressant medication. It follows that an MS population will include those whose current depressive symptoms meet threshold for depression diagnosis, plus those who previously met diagnostic criteria for depression and have been treated such that depressive symptoms have improved below that threshold. We examined a large MS population to establish a multi-attribute estimate of depression, taking into account probable depression on HADS-D, as well as anti-depressant medication use and co-morbidity data reporting current treatment for depression. We then studied associations with demographic and health status measures and the trajectories of depressive symptoms over time.MethodsParticipants were recruited into the UK-wide Trajectories of Outcome in Neurological Conditions-MS (TONiC-MS) study, with demographic and disease data from clinical records, PROMs collected at intervals of at least 9 months, as well as co-morbidities and medication. Interval level conversions of PROM data followed Rasch analysis. Logistic regression examined associations of demographic characteristics and symptoms with depression. Finally, a group-based trajectory model was applied to those with depression.ResultsBaseline data in 5633 participants showed the prevalence of depression to be 25.3% (CI: 24.2-26.5). There were significant differences in prevalence by MS subtype: relapsing 23.2% (CI: 21.8- 24.5), primary progressive 25.8% (CI: 22.5-29.3), secondary progressive 31.5% (CI: 29.0-34.0); disability: EDSS 0-4 19.2% (CI: 17.8-20.6), EDSS ≥4.5 31.9% (CI: 30.2-33.6); and age: 42-57 years 27.7% (CI: 26.0-29.3), above or below this range 23.1% (CI: 21.6-24.7). Fatigue, disability, self-efficacy and self esteem correlated with depression with a large effect size (>.8) whereas sleep, spasticity pain, vision and bladder had an effect size >.5. The logistic regression model (N=4938) correctly classified 80% with 93% specificity: risk of depression was increased with disability, fatigue, anxiety, more comorbidities or current smoking. Higher self-efficacy or self esteem and marriage reduced depression. Trajectory analysis of depressive symptoms over 40 months in those with depression (N=1096) showed three groups: 19.1% with low symptoms, 49.2% with greater symptoms between the threshold of possible and probable depression, and 31.7% with high depressive symptoms. 29.9% (CI: 27.6-32.3) of depressed subjects were untreated, conversely of those treated, 26.1% still had a symptom level consistent with a probable case (CI: 23.5-28.9).ConclusionA multi-attribute estimate of depression in MS is essential because using only screening questionnaires, diagnoses or antidepressant medication all under-estimate the true prevalence. Depression affects 25.3% of those with MS, almost half of those with depression were either untreated or still had symptoms indicating probable depression despite treatment. Services for depression in MS must be pro-active and flexible, recognising the heterogeneity of outcomes and reaching out to those with ongoing symptoms