Verbal recall after mild traumatic brain injury: Sensitivity of the rapid screen of concussion

The present study aimed to determine whether including a sensitive test of immediate and delayed recall would improve the diagnostic validity of the Rapid Screen of Concussion (RSC) in mild Traumatic Brain Injury (mTBI) versus orthopaedic clinical samples. Two studies were undertaken. In Study 1, the performance of 156 mTBI and 145 orthopaedic participants was analysed to identify the number of individuals who performed at ceiling on the verbal memory subtest of the RSC, as this test required immediate and delayed recall of only five words. A second aim was to determine the sensitivity and specificity levels of the RSC. Study 2 aimed to examine whether replacement of the verbal memory subtest with the 12-word Hopkins Verbal Learning Test (HVLT) could improve the sensitivity of the RSC in a new sample of 26 mTBI and 30 orthopaedic participants. Both studies showed that orthopaedic participants outperformed mTBI participants on each of the selected measures. Study 1 showed that 14% of mTBI participants performed at ceiling on the immediate and 21.2% on delayed recall test. Performance on the original battery yielded a sensitivity of 82%, specificity of 80% and overall correct classification of 81.5% participants. In Study 2, inclusion of the HVLT improved sensitivity to a level of 88.5%, decreased specificity to a level of 70% and resulted in an overall classification rate of 80%. It was concluded that although inclusion of the five-word subtest in the RSC can successfully distinguish concussed from non-concussed individuals, use of the HVLT in this protocol yields a more sensitive measure of subtle cognitive deficits following mTBI.

Lifetime prevalence estimates of major depression: An indirect estimation method and a quantification of recall bias

The measurement of lifetime prevalence of depression in cross-sectional surveys is biased by recall problems. We estimated it indirectly for two countries using modelling, and quantified the underestimation in the empirical estimate for one. A microsimulation model was used to generate population-based epidemiological measures of depression. We fitted the model to 1-and 12-month prevalence data from the Netherlands Mental Health Survey and Incidence Study (NEMESIS) and the Australian Adult Mental Health and Wellbeing Survey. The lowest proportion of cases ever having an episode in their life is 30% of men and 40% of women, for both countries. This corresponds to a lifetime prevalence of 20 and 30%, respectively, in a cross-sectional setting (aged 15-65). The NEMESIS data were 38% lower than these estimates. We conclude that modelling enabled us to estimate lifetime prevalence of depression indirectly. This method is useful in the absence of direct measurement, but also showed that direct estimates are underestimated by recall bias and by the cross-sectional setting.

Precision-recall operating characteristic (P-ROC) curves in imprecise environments

Traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. In this paper, we consider the case of imprecise environments, where little may be known about these factors and they may well vary significantly when the system is applied. Specifically, the use of precision-recall analysis is investigated and compared to the more well known performance measures such as error-rate and the receiver operating characteristic (ROC). We argue that while ROC analysis is invariant to variations in class priors, this invariance in fact hides an important factor of the evaluation in imprecise environments. Therefore, we develop a generalised precision-recall analysis methodology in which variation due to prior class probabilities is incorporated into a multi-way analysis of variance (ANOVA). The increased sensitivity and reliability of this approach is demonstrated in a remote sensing application.

Investigating Concept Mapping and Stimulated Recall to Reveal Academic Teacher Beliefs and how they are enacted through learning designs for the Web

Increasingly, academic teachers are designing their own web sites to add value to or replace other forms of university teaching. These web sites are tangible and dynamic constructions that represent the teachers thinking and decisions derived from an implicit belief system about teaching and learning. The emphasis of this study is to explore the potential of the research techniques of concept-mapping and stimulated recall to locate the implicit pedagogies of academic teachers and investigate how they are enacted through the learning designs of their web sites. The rationale behind such an investigation is that once these implicit belief systems are made visible, then conversations can commence about how these beliefs are transformed into practice, providing a potent departure point for academic development.