Book Review: Léa Veinstein, Les philosophes lisent Kafka. Benjamin, Arendt, Adorno, Anders

A book review of Léa Veinstein, Les philosophes lisent Kafka: Benjamin, Arendt, Adorno, Anders (Éditions de la Maison des sciences de l'homme: Paris, 2019)

The employability strengths American Millennials contribute to the health administration workforce: A workplace readiness study at two California public universities

This qualitative study examined employability, also referred to as workplace readiness strengths in this article, for American Millennial health administration baccalaureates. The primary researcher used data collected from focus groups and interviews conducted at two certified Association of University Programs in Health Administration (AUPHA) programs in California, USA. Participants (n = 71) belonged to one of 4 distinct groups: (a) health administration faculty, (b) internship preceptors, (c) alumni, and (d) undergraduate students (interns) enrolled in their internship program. Thematic content analysis was used to evaluate the collected qualitative data, after which descriptive statistics was applied to calculate the frequencies of emergent themes. The National Association of Colleges and Employers (NACE) Career Readiness Competencies, an employer and university validated list of Career Readiness Competencies for a Career-Ready Workforce, was used as a comparative framework for the workplace readiness strengths provided in the qualitative data. Six strength-based themes emerged, two of which comparatively aligned with two of the NACE Career Readiness Competencies. However, respondents indicated that the rest of the NACE Career Competencies were not overtly expressed by Millennials as workplace strengths and should be embedded into the health administration curriculum. This invaluable information can be used to update the AUPHA health administration curriculum and help their undergraduate students increase their employability index scores

Biomechanical factors associated with jump height: a comparison of cross-sectional and pre-to-post training change findings

Previous studies investigating the biomechanical factors associated with maximal countermovement jump height have typically utilised cross-sectional data. An alternative but less common approach is to use pre-to-post training change data, where the relationship between an improvement in jump height and a change in a factor is examined more directly. Our study compared the findings of these approaches. Such an evaluation is necessary because cross-sectional studies are currently a primary source of information for coaches when examining what factors to train to enhance performance. The countermovement jump of forty four males was analysed before and after an eight week training intervention. Correlations with jump height were calculated using both cross-sectional (pre-training data only) and pre-to-post training change data. Eight factors identified in the cross-sectional analysis were not significantly correlated with a change in jump height in the pre-to-post analysis. Additionally, only six of eleven factors identified in the pre-to-post analysis were identified in the cross-sectional analysis. These findings imply that: (a) not all factors identified in a cross-sectional analysis may be critical to jump height improvement, and (b) cross-sectional analyses alone may not provide an insight into all of the potential factors to train to enhance jump height. Coaches must be aware of these limitations when examining cross-sectional studies to identify factors to train to enhance jump ability. Additional findings highlight that while exercises prescribed to improve jump height should aim to enhance concentric power production at all joints, a particular emphasis on enhancing hip joint peak power may be warranted

CAN BIOMECHANICAL DIAGNOSTIC PROFILING IDENTIFY THE EFFECTIVENESS OF SPECIFIC TRAINING EXERCISES?

This study investigated the use of a diagnostic and prescriptive pathway that aims to determine the effectiveness of specific training exercises. The model was tested by examining if the effects of drop jump (DJ) training on countermovement jump (CMJ) performance could be explained by the degree to which performance determining factors (PDFs) for the CMJ were overloaded. Participants trained with DJ for 8 weeks yet no change in CMJ performance occurred. Of the 4 CMJ PDFs identified only hip rate of power development was overloaded by the DJ and none were enhanced with training. The results imply that the pathway was effective in identifying whether DJ training would enhance participants CMJ performance. The model could be used to determine if a given exercise would enhance a specific group of athletes prior to initiating training

CHANGES IN HIP FORCE VECTOR AFTER ATHLETIC GROIN PAIN REHABILITATION DURING A RUNNING CUT

The purpose of this study was to examine changes in hip force vectors after successful athletic groin pain rehabilitation. Forty athletes with athletic groin pain that underwent a rehabilitation intervention participated in this study. Hip force magnitude, direction and their combination were examined using a continuous waveform analysis. Hip posterior and medial force at the start and end of the movement decreased following rehabilitation, while superior forces increased (over most of the movement cycle). Findings suggest that athletes with groin pain benefit from a rehabilitation intervention that decreases posterior and medial hip joint forces

Identification of movement strategies in vertical jumps

The primary aim of this study is to compare the ability of three commonly used clustering techniques to identify movement strategies within countermovement jumps. A secondary aim is to interpret the identified movement strategies. A hierarchical, k-means using non- and normalized subject scores and an Expectation-Maximization approach using normalized subject scores were examined. The ability to identify movement strategies was measured using the r2-value of a regression model to describe jump height. Clusters of the best clustering solution were examined for differences. Hierarchical clustering utilizing normalized subject scores to generate 4 clusters appears to be the most suitable technique. The generated clusters demonstrated clear defining characteristics

Classification of continuous vertical ground reaction forces

The aim of this study is to assess and compare the performance of com- monly used hierarchical, partitional (k-means) and Gaussian model-based (Expectation-Maximization algorithm) clustering techniques to appropriately identify subgroup patterns within vertical ground reaction force data, using a continuous waveform analysis. In addition, we also compared the perfor- mance across each technique using normalized and non-normalization input scores. Both generated and real data (one hundred-and twenty two verti- cal jumps) were analyzed. The performance of each cluster technique was measured by assessing the ability to explain variances in jump height using a stepwise regression analysis. Only k-means (normalized scores; 82 %) and hierarchical clustering (normalized scores; 85 %) were able to extend the ability to describe variances in jump height beyond that achieved using the group analysis (i.e. one cluster; 78 %). Further, our findings strongly indicate the need to normalize the input data (similarity measure) when clustering. In contrast to the group analysis, the subgroup analysis was able to iden- tify cluster specific phases of variance, which improved the ability to explain variances in jump height, due to the identification of cluster specific predictor variables. Our findings therefore highlight the benefit of performing a subgroup analysis and may explain, at least in part, the contrasting findings between previous studies that used a single group level of analysis

IDENTIFICATION OF MOVEMENT STRAGEGIES IN VERTICAL JUMPS

The primary aim of this study is to compare the ability of three commonly used clustering techniques to identify movement strategies within countermovement jumps. A secondary aim is to interpret the identified movement strategies. A hierarchical, k-means using nonand normalized subject scores and an Expectation-Maximization approach using normalized subject scores were examined. The ability to identify movement strategies was measured using the r2-value of a regression model to describe jump height. Clusters of the best clustering solution were examined for differences. Hierarchical clustering utilizing normalized subject scores to generate 4 clusters appears to be the most suitable technique. The generated clusters demonstrated clear defining characteristics