Healthcare quality improvement and ‘work engagement’; concluding results from a national, longitudinal, cross-sectional study of the ‘Productive Ward-Releasing Time to Care’ programme

Concerns about patient safety and reducing harm have led to a particular focus on initiatives that improve healthcare quality. However Quality Improvement (QI) initiatives have in the past typically faltered because they fail to fully engage healthcare professionals, resulting in apathy and resistance amongst this group of key stakeholders. Productive Ward: Releasing Time to Care (PW) is a ward-based QI programme created to help ward-based teams redesign and streamline the way that they work; leaving more time to care for patients. PW is designed to engage and empower ward-based teams to improve the safety, quality and delivery of care

The Ethics of Engagement in an Age of Austerity: A Paradox Perspective

Our contribution in this paper is to highlight the ethical implications of workforce engagement strategies in an age of austerity. Hard or instrumentalist approaches to workforce engagement create the potential for situations where engaged employees are expected to work ever longer and harder with negative outcomes for their well-being. Our study explores these issues in an investigation of the enactment of an engagement strategy within a UK Health charity, where managers and workers face paradoxical demands to raise service quality and cut costs. We integrate insights from engagement, paradox, and ethic of care literatures, to explore these paradoxical demands—illustrating ways in which engagement experiences become infused with tensions when the workforce faces competing requirements to do 'more with less' resources. We argue that those targeted by these paradoxical engagement strategies need to be supported and cared for, embedded in an ethic of care that provides explicit workplace resources for helping workers and managers cope with and work through corresponding tensions. Our study points to the critical importance of support from senior and frontline managers for open communications and dialogue practices

Scanning tunneling state recognition with multi-class neural network ensembles

One of the largest obstacles facing scanning probe microscopy is the constant need to correct flaws in the scanning probe in situ. This is currently a manual, time-consuming process that would benefit greatly from automation. Here, we introduce a convolutional neural network protocol that enables automated recognition of a variety of desirable and undesirable scanning tunneling tip states on both metal and nonmetal surfaces. By combining the best performing models into majority voting ensembles, we find that the desirable states of H:Si(100) can be distinguished with a mean precision of 0.89 and an average receiver-operator-characteristic curve area of 0.95. More generally, high and low-quality tips can be distinguished with a mean precision of 0.96 and near perfect area-under-curve of 0.98. With trivial modifications, we also successfully automatically identify undesirable, non-surface-specific states on surfaces of Au(111) and Cu(111). In these cases, we find mean precisions of 0.95 and 0.75 and area-under-curves of 0.98 and 0.94, respectively. Provided that training data are available, these ensembles therefore enable fully autonomous scanning tunneling state recognition for a wide range of typical scanning conditions

Scanning tunneling state recognition with multi-class neural network ensembles

One of the largest obstacles facing scanning probe microscopy is the constant need to correct flaws in the scanning probe in situ. This is currently a manual, time-consuming process that would benefit greatly from automation. Here, we introduce a convolutional neural network protocol that enables automated recognition of a variety of desirable and undesirable scanning tunneling tip states on both metal and nonmetal surfaces. By combining the best performing models into majority voting ensembles, we find that the desirable states of H:Si(100) can be distinguished with a mean precision of 0.89 and an average receiver-operator-characteristic curve area of 0.95. More generally, high and low-quality tips can be distinguished with a mean precision of 0.96 and near perfect area-under-curve of 0.98. With trivial modifications, we also successfully automatically identify undesirable, non-surface-specific states on surfaces of Au(111) and Cu(111). In these cases, we find mean precisions of 0.95 and 0.75 and area-under-curves of 0.98 and 0.94, respectively. Provided that training data are available, these ensembles therefore enable fully autonomous scanning tunneling state recognition for a wide range of typical scanning conditions

Effect of Friction Stir Processing on Microstructure and Mechanical Properties of a Cast-magnesium-rare Earth Alloy

Single-pass friction stir processing (FSP) was used to increase the mechanical properties of a cast Mg-Zn-Zr-rare earth (RE) alloy, Elektron 21. A fine grain size was achieved through intense plastic deformation and the control of heat input during processing. The effects of processing and heat treatment on the mechanical and microstructural properties were evaluated. An aging treatment of 16 hours at 200 °C resulted in a 0.2 pct proof stress of 275 MPa in the FSP material, a 61 pct improvement over the cast + T6 condition