Critical thinking and clinical reasoning in new graduate occupational therapists: a phenomenological study.

The aim of this study was to examine, understand and conceptualise the critical thinking and clinical reasoning adopted by new graduate occupational therapists as they enter the workforce to become newly autonomous practitioners. The study obtained the perspectives of new graduates, their supervisors and service managers on the means by which critical thinking and clinical reasoning develop to meet the expectations of employers. Factors which impeded the transition between new graduate and autonomous practitioner were identified and explored. Ethical approval was obtained to conduct the study. The study adopted a qualitative phenomenological research approach; Interpretative Phenomenological Analysis (IPA), which informed framing, data gathering and analysis. Semi-structured interviews were conducted with new graduates (n=6), supervisors (n=7) and managers (n=7) from multiple sites within one National Health Service Board. Interviews were transcribed verbatim from audio-recordings. The findings indicate that new graduates are expected to develop critical thinking and clinical reasoning in a manner that might challenge traditional conceptualisations of the transitioning process. A phenomenon, historically named the shock of practice, was reflected on by therapists in each phase of the study and adaptive and mal-adaptive responses to this in the thinking and behaviour of new graduates was identified. The clinical supervisor-supervisee relationship appeared to be the key source of support, and the supervisor the most significant knowledge resource, for new graduates. This relationship was supplemented by both peer support and Preceptorship. Discharge planning was a significant source of anxiety and development of an algorithm to support this process is proposed. Recommendations for further research and theoretical implications for practice and undergraduate education are discussed

IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters

In this work, we study robust deep learning against abnormal training data from the perspective of example weighting built in empirical loss functions, i.e., gradient magnitude with respect to logits, an angle that is not thoroughly studied so far. Consequently, we have two key findings: (1) Mean Absolute Error (MAE) Does Not Treat Examples Equally. We present new observations and insightful analysis about MAE, which is theoretically proved to be noise-robust. First, we reveal its underfitting problem in practice. Second, we analyse that MAE's noise-robustness is from emphasising on uncertain examples instead of treating training samples equally, as claimed in prior work. (2) The Variance of Gradient Magnitude Matters. We propose an effective and simple solution to enhance MAE's fitting ability while preserving its noise-robustness. Without changing MAE's overall weighting scheme, i.e., what examples get higher weights, we simply change its weighting variance non-linearly so that the impact ratio between two examples are adjusted. Our solution is termed Improved MAE (IMAE). We prove IMAE's effectiveness using extensive experiments: image classification under clean labels, synthetic label noise, and real-world unknown noise. We conclude IMAE is superior to CCE, the most popular loss for training DNNs.Comment: Updated Version. IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters Code: \url{https://github.com/XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE}. Please feel free to contact for discussions or implementation problem

Adaptive HIV-1 evolutionary trajectories are constrained by protein stability

Despite the use of combination antiretroviral drugs for the treatment of HIV-1 infection, the emergence of drug resistance remains a problem. Resistance may be conferred either by a single mutation or a concerted set of mutations. The involvement of multiple mutations can arise due to interactions between sites in the amino acid sequence as a consequence of the need to maintain protein structure. To better understand the nature of such epistatic interactions, we reconstructed the ancestral sequences of HIV-1's Pol protein, and traced the evolutionary trajectories leading to mutations associated with drug resistance. Using contemporary and ancestral sequences we modelled the effects of mutations (i.e. amino acid replacements) on protein structure to understand the functional effects of residue changes. Although the majority of resistance-associated sequences tend to destabilise the protein structure, we find there is a general tendency for protein stability to decrease across HIV-1's evolutionary history. That a similar pattern is observed in the non-drug resistance lineages indicates that non-resistant mutations, for example, associated with escape from the immune response, also impacts on protein stability. Maintenance of optimal protein structure therefore represents a major constraining factor to the evolution of HIV-1

Online multiple hypothesis testing for reproducible research

Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and technological applications, an additional complexity is that hypotheses are tested in an online manner, one-by-one over time. However, traditional procedures that control the FDR, such as the Benjamini-Hochberg procedure, assume that all p-values are available to be tested at a single time point. To address these challenges, a new field of methodology has developed over the past 15 years showing how to control error rates for online multiple hypothesis testing. In this framework, hypotheses arrive in a stream, and at each time point the analyst decides whether to reject the current hypothesis based both on the evidence against it, and on the previous rejection decisions. In this paper, we present a comprehensive exposition of the literature on online error rate control, with a review of key theory as well as a focus on applied examples. We also provide simulation results comparing different online testing algorithms and an up-to-date overview of the many methodological extensions that have been proposed.Comment: Updated in response to reviewer comment