Sights and insights: Vocational outdoor students’ learning

Outdoor leader and adventure sport education in the United Kingdom has been characterized by an over-emphasis on technical skills at the expense of equally important, but often marginalized intra- and inter-personal skills necessary for contemporary outdoor employment. This study examined the lived experience of vocational outdoor students in order, firstly, to identify what was learned about the workplace through using reflective practice and, secondly, what was learned about reflective practice through this experience. The study used a purposive sample of students (n=15) who were invited to maintain reflective journals during summer work experience, and this was followed up with semi-structured interviews. Manual Interpretative Phenomenological Analysis (IPA) revealed that in the workplace setting students used reflective practice to understand and develop technical proficiency, support awareness of the value of theory, and acted as a platform to express emergent concepts of ‘professionalism’. Lessons about reflective practice emphasized its value in social settings, acknowledging different ways of reflection, and understanding and managing professional life beyond graduation

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection.

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data