The purpose of this study was to explore the experiences and impact of peer-to-peer shadowing as a technique to develop nurse middle managers’ clinical leadership practices. A qualitative descriptive study was conducted to gain insight into the experiences of nurse middle managers using semi-structured interviews. Data were analysed into codes using constant comparison and similar codes were grouped under sub-themes and then into four broader themes. Peer-to-peer shadowing facilitates collective reflection-in-action and enhances an “investigate stance” while acting. Nurse middle managers begin to curb the caring disposition that unreflectively urges them to act, to answer the call for help in the here and now, focus on ad hoc “doings”, and make quick judgements. Seeing a shadowee act produces, via a process of social comparison, a behavioural repertoire of postponing reactions and refraining from judging. Balancing the act of stepping in and doing something or just observing as well as giving or withholding feedback are important practices that are difficult to develop.
The prediction of the running injuries based on selfreported training data on load is difficult. At present, coaches and researchers have no validated system to predict if a runner has an increased risk of injuries. We aim to develop an algorithm to predict the increase of the risk of a runner to sustain an injury. As a first step Self-reported data on training parameters and injuries from high-level runners (duration=37 weeks, n=23, male=16, female=7) were used to identify the most predictive variables for injuries, and train a machine learning tree algorithm to predict an injury. The model was validated by splitting the data in training and a test set. The 10 most important variables were identified from 85 possible variables using the Random Forest algorithm. To predict at an earliest stage, so the runner or the coach is able to intervene, the variables were classified by time to build tree algorithms up to 7 weeks before the occurrence of an injury. By building machine learning algorithms using existing self-reported training data can enable prospective identification of high-level runners who are likely to develop an injury. Only the established prediction model needs to be verified as correct.
It is unknown how movement patterns that are learned carry over to the field. The objective was to deter- mine whether training during a jump-landing task would transfer to lower extremity kinematics and kinetics during sidestep cutting.Methods Forty healthy athletes were assigned to the ver- bal internal focus (IF, n = 10), verbal external focus (EF, n = 10), video (VI, n = 10) or control (CTRL, n = 10) group. A jump-landing task was performed as baseline followed by training blocks (TR1 and TR2) and a post-test. Group-spe- cific instructions were given in TR1 and TR2. In addition, participants in the IF, EF and VI groups were free to ask for feedback after every jump during TR1 and TR2. Retention was tested after 1 week. Transfer of learned skill was deter- mined by having participants perform a 45° unanticipated sidestep cutting task. 3D hip, knee and ankle kinematics and kinetics were the main outcome measures.Results During sidestep cutting, the VI group showed greater hip flexion ROM compared to the EF and IF groups (p < 0.001). The EF (p < 0.036) and VI (p < 0.004) groups had greater knee flexion ROM compared to the IF group. Conclusions Improved jump-landing technique car- ried over to sidestep cutting when stimulating an external attentional focus combined with self-controlled feedback. Transfer to more sport-specific skills may demonstrate potential to reduce injuries on the field. Clinicians and practitioners are encouraged to apply instructions that stimulate an external focus of attention, of which visual instructions seem to be very powerful.
