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Purpose The purpose of this paper is to interrogate and develop the conceptualisation of discriminatory abuse in safeguarding adults policy and practice beyond the current interpersonal definition. The paper draws on Safeguarding Adults Reviews (SARs) that refer to discriminatory abuse or safeguarding practice with adults who have care and support needs and protected characteristics. Design/methodology/approach A search of the national network repository of SARs identified 27 published reviews for inclusion. The contents were thematically analysed to understand how discrimination was experienced in these cases. Fricker’s “Epistemic Injustice” theory was adopted as a conceptual framework, informing the analysis of findings. Findings Evidence from SARs provides a challenge to the interpersonal emphasis on language and behaviour in national policy. Although the reviews acknowledge that interpersonal abuse occurs, a close reading spotlights practitioner and institutional bias, and inattention to social, structural and contextual factors. The silence on these matters in policy provides a narrow frame for interpreting such abuse. This suggests significant potential for epistemic injustice and signals a need to acknowledge these social, structural and contextual factors in safeguarding practice. Originality/value Discriminatory abuse is an under-researched and under-utilised category of abuse in safeguarding adults practice. The paper adopts Fricker’s theory of “Epistemic Injustice” to highlight the silencing potential of current policy approaches to discriminatory abuse to suggest a more inclusive and structural framing for safeguarding practice with those targeted due to their protected characteristics.

Purpose Discriminatory abuse has been a distinct category of abuse in safeguarding adults policy since 2000, but it is rarely used in practice, according to recent official statistics. As part of a larger project, the authors undertook a literature review to clarify the concept, explore reasons for low reporting and consider recommendations for practice. The purpose of this paper is to present the findings of this literature review. Design/methodology/approach This literature review comprises 35 sources, which were identified using three academic databases, reference harvesting and sector-specific websites. Findings were developed through thematic analysis of the data. Findings The literature review demonstrates that definitions of discriminatory abuse stretch from an interpersonal emphasis in policy documents to a more structural approach. There are open questions about the status of discriminatory abuse as a category of abuse due to the complicated interface between discriminatory motivations and the abusive acts through which they are experienced. A range of factors can obscure its identification, particularly the hidden, stigmatised and normalised nature of discriminatory abuse. Some recommendations for practice are identified, but more work is needed to develop the practice vocabulary and required skills. Originality/value This study brings together existing research on discriminatory abuse to argue that it is time to revive this understanding of abuse and develop safeguarding practice with adults who have protected characteristics.

Care homes for older people may become places of deprivation of liberty due to their residents’ lack of capacity to consent to their stay. The Optional Protocol to the Convention Against Torture requires States Parties to establish National Preventive Mechanisms to undertake visits to all places of deprivation of liberty to prevent torture and other ill treatment. This paper discusses the ways in which National Preventive Mechanisms report on human rights concerns in older people’s care homes across 26 Council of Europe states. A framework established by the European Committee for the Prevention of Torture provides a starting point for this analysis. Common themes around restraint, ill treatment, safeguards for involuntary placements, and resourcing issues emerge. Against a backdrop of concerns heightened by the Covid-19 pandemic, the paper concludes that a narrow focus on individual human rights needs to be supplemented by addressing the structural issues on which the more broadly conceived rights of residents of care homes for older people depend.

This article presents a case study of an innovative course requested, funded and designed by the West London Social Work Teaching Partnership (WLSWTP). Due to difficulties in recruiting social work...

There is an ever-growing number of photovoltaic (PV) installations in the US and worldwide. Many utilities do not have complete or up-to-date information of the PVs present within their grids. This research presents a deep neural network approach for estimating PV size, tilt, and azimuth using only behind-the-meter data. It is found that the proposed deep neural network (DNN) method can estimate PV size with an error of 2.09% in a data set with fixed tilt and azimuth values and 3.98% in a data set with varying tilt and azimuths. This is a lower error than the benchmark linear regression approach. A net load data resolution of 1 min provides the lowest error when estimating the PV size. The proposed DNN is also reasonably robust to erroneous training data. When applied to estimate PV tilt and azimuth, the proposed method achieves a mean absolute percentage error of 10.1% and 2.8% respectively. These error metrics are 2.0 × and 3.7 × lower, respectively, than the benchmark linear regression achieves. It was observed that a higher data resolution (1 min) does not provide significant gains in accuracy. It is recommended that a data resolution of 60 min is used to reduce the effects of phenomena such as cloud enhancements. The proposed deep neural network approach is also highly robust, maintaining reasonable accuracy with high levels of mislabeled training data.

Increased penetration of behind-the-meter (BTM) PV installations can cause numerous challenges in planning and operation of distribution systems. Utilities must accurately record the installed PVs in their territory and keep their PV database updated. However, many utilities do not have enough visibility on the actual installed PVs due the growing number of unauthorized PV installations as well as the complexity of data tracking and updating the databases even for authorized PVs. In this paper, a data-driven classification method is proposed for detecting BTM PV installation using convolutional neural networks and synthetic net load profiles generated from AMI data. The network is trained and tested on 50 folds of the dataset and the testing classification accuracy per each fold is calculated. Results show that the median of per-fold testing accuracies is 98.9%. In terms of average error, only 0.7% of the customers with PV are not detected. This is significantly less than the 6% error in the next best method. The impact of training data parameters, such as the size of dataset and label errors on the accuracy and computational time of the method is also studied and characterized. Using only the available AMI data, the proposed method can help utilities accurately monitor BTM PV systems and keep their databases updated and thus avoid the costs of operation and planning errors.