Reflection is considered necessary and beneficial within career learning and is deemed to be a condition for successful career-identity development. Indeed, reflection is generally seen as a key competency in learning how to respond effectively to a complex and dynamic post-modern world in which individuals are increasingly exposed to risk. Paradoxically however, reflection can itself form a risk when it results in rumination. It is therefore important to identify the conditions and personal (risk) factors that make reflection a detrimental or beneficial activity and to identify elements within career learning interventions that promote benefit. The purpose here is to increase awareness about reflective versus ruminative processes and promote responsible use of interventions that aim to stimulate reflection in the process of career-identity formation. Based on the “career writing” method, the authors conclude that a successful career intervention must especially provide good facilitation and a safe holding environment. https://doi.org/10.1177/1038416216670675 LinkedIn: https://www.linkedin.com/in/reinekke-lengelle-phd-767a4322/
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Animal welfare is a multidimensional phenomenon and currently its on-farm assessment requires complex, multidimensional frameworks involving farm audits which are time-consuming, infrequent and expensive. The core principle of precision agriculture is to use sensor technologies to improve the efficiency of resource use by targeting resources to where they give a benefit. Precision livestock farming (PLF) enables farm animal management to move away from the group level to monitoring and managing individual animals. A range of precision livestock monitoring and control technologies have been developed, primarily to improve livestock production efficiency. Examples include using camera systems monitoring the movement of housed broiler chickens to detect problems with feeding systems or disease and leg-mounted accelerometers enabling the detection of the early stages of lameness in dairy cows. These systems are already improving farm animal welfare by, for example, improving the detection of health issues enabling more rapid treatment, or the detection of problems with feeding systems helping to reduce the risk of hunger. Environmental monitoring and control in buildings can improve animal comfort, and automatic milking systems facilitate animal choice and improve human-animal interactions. Although these precision livestock technologies monitor some parameters relevant to farm animal welfare (e.g. feeding, health), none of the systems yet provide the broad, multidimensional integration that is required to give a complete assessment of an animal’s welfare. However, data from PLF sensors could potentially be integrated into automated animal welfare assessment systems, although further research is needed to define and validate this approach.
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