Islamophobia in the National Health Service: an ethnography of institutional racism in PREVENT's counter‐radicalisation policy

In 2015, the UK government made its counter‐radicalisation policy a statutory duty for all National Health Service (NHS) staff. Staff are now tasked to identify and report individuals they suspect may be vulnerable to radicalisation. Prevent training employs a combination of psychological and ideological frames to convey the meaning of radicalisation to healthcare staff, but studies have shown that the threat of terrorism is racialised as well. The guiding question of our ethnography is: how is counter‐radicalisation training understood and practiced by healthcare professionals? A frame analysis draws upon 2 years of ethnographic fieldwork, which includes participant observation in Prevent training and NHS staff interviews. This article demonstrates how Prevent engages in performative colour‐blindness – the active recognition and dismissal of the race frame which associates racialised Muslims with the threat of terrorism. It concludes with a discussion of institutional racism in the NHS – how racialised policies like Prevent impact the minutia of clinical interactions; how the pretence of a ‘post‐racial’ society obscures institutional racism; how psychologisation is integral to the performance of colour‐blindness; and why it is difficult to address the racism associated with colourblind policies which purport to address the threat of the Far‐Right

Keeping our mouths shut: the fear and racialized self-censorship of British healthcare professionals in PREVENT training

The PREVENT policy introduced a duty for British health professionals to identify and report patients they suspect may be vulnerable towards radicalisa- tion. Research on PREVENT’s impact in healthcare is scant, especially on the lived experiences of staff. This study examined individual interviews with 16 critical National Health Service (NHS) professionals who participated in mandatory PRE- VENT counter-radicalisation training, half of whom are Muslims. Results reveal two themes underlying the self-censorship healthcare staff. The first theme is fear, which critical NHS staff experienced as a result of the political and moral subscript underlying PREVENT training: the ‘good’ position is to accept the PREVENT duty, and the ‘bad’ position is to reject it. This fear is experienced more acutely by British Muslim healthcare staff. The second theme relates to the structures which extend beyond PREVENT but nonetheless contribute to self-censorship: distrustful settings in which the gaze of unknown colleagues stifles personal expression; reluctant trainers who admit PREVENT may be unethical but nonetheless relinquish responsibility from the act of training; and socio-political conditions affecting the NHS which overwhelm staff with other concerns. This paper argues that counter- terrorism within healthcare settings may reveal racist structures which dispropor- tionality impact British Muslims, and raises questions regarding freedom of conscience

Prevent: what is pre-criminal space?

Prevent is a UK-wide programme within the government’s anti-terrorism strategy aimed at stopping individuals from supporting or taking part in terrorist activities. NHS England’s Prevent Training and Competencies Framework requires health professionals to understand the concept of pre-criminal space. This article examines pre-criminal space, a new term which refers to a period of time during which a person is referred to a specific Prevent-related safeguarding panel, Channel. It is unclear what the concept of pre-criminal space adds to the Prevent programme. The term should be either clarified or removed from the Framework

What is pre-criminal space?

Prevent is a UK-wide programme within the government's anti-terrorism strategy aimed at stopping individuals from supporting or taking part in terrorist activities. NHS England's Prevent Training and Competencies Framework requires health professionals to understand the concept of pre-criminal space. This article examines pre-criminal space, a new term which refers to a period of time during which a person is referred to a specific Prevent-related safeguarding panel, Channel. It is unclear what the concept of pre-criminal space adds to the Prevent programme. The term should be either clarified or removed from the Framework

Use of Air Infiltration in Swine Housing Ventilation System Design

The objective of this research was to develop and analyze the procedure for using recent air infiltration (AI) data collected from commercial swine finishing rooms (SFRs) in the design of negative pressure mechanical ventilation systems (VSs). Air infiltration is an integral part of any ventilation process. Infiltration reduces the pressure differential across planned inlets and at very low pressure differences, cold air jets may drop directly on the animals causing significant discomfort. In this article, a design procedure is proposed for swine housing ventilation systems with the influence of air infiltration included. The method was used on one SFR for which air infiltration data was collected by in-field testing. The air-jet throw, jet momentum number, a newly developed coverage factor, and Archimedes number were used to assess the influence of infiltration on predicted air-jet and fresh-air distribution and to help guide the design of planned inlets in SFR VSs with known infiltration. The analysis completed quantifies the severity of AI on air-jet and air distribution performance, and suggests that for the analysis room to ventilate properly requires a 50% reduction in AI levels beyond field measured curtain and fan infiltration. The analysis completed suggests a method for systematically planning three-dimensional ceiling inlet placement and operation and provides design guidance for new ceiling inlets suitable for SFR VSs

GC-MS and FTIR analysis of methanolic leaf extract of Rhynchosia minima (L.) DC.

The current analysis was carried out to determine the chemical components in the leaves of R. minima (L.) DC. The GC-MS analysis of methanolic leaves extract of R. Minima indicated the presence of 19 compounds. The prevailing compounds of R. minima leaves were 1Pentadecene (14.31), alpha. Bisabolol (10.39%), 1Heptadecene (9.78%), Cyclohexene,4 (1,5dimethyl1,4hexadienyl (7.06%), 3Hexadecene (Z) (8.10%), Caryophyllene (6.58%), Neophytadiene (5.16%), Humulene (1.91%), Naphthalene,1,2,3,5,6,8 a-hexahydro-4,7-dimethyl (3.72%), Hexadecanoic acid, methyl ester (2.09%), Pentadecanone (3.13%), 8-Octadecanone (4.02%),1-Nonadecene (4.16%), Spiro[4.5]dec-6-en-8-one,1,7-dimethyl-4-(1-methylethyl (2.97%), Neophytadiene (2.24%),(E)-. beta.-Famesene (1.92%), Cyclohexene,4-[(1E)-1,5-dimethyl-1,4-hexadien (1.80%), Cyclohexane,octyl (1.45%), beta Bisabolene (9.21%). These compounds have antibacterial, antifungal, antioxidant, hemolytic, insecticidal, and lubricant activity. Fourier Transform Infra-Red Spectroscopy (FTIR) leaf anlysis of R. minima shows lipid, protein, phosphate ion, carboxylic acid, hydroxy compound, aliphatic bromo compounds. The present study revealed that R. minima leaves represent various types of bioactive compounds. 1-Heptadecene with antibiotic activity, 8-Octadecanone shows antimicrobial activity and hexadecanoic acid, nematicide, antibiotic, antioxidant, hypocholesterolemic production of methyl ester

An Analytical study of Foreign Direct Investment In Indian Retail Sector

As per the current regulatory regime, retail traiding except under single-brand product retailing- FDI up to 51%, under the government route is prohibited in India. Simply put, for a company to able to get foreign fundings, products sold by it to the general public should only be of a ‘single-brand’, this condition being addition to a few other conditions to be adhered to. That explains why we do not have a Harrods in Delhi. India being a signatory to WTO’s General Agreement on Trade in Services, which include wholesale and retailing services, had to open up the retail trade sector to foreign investment. There were initial reservations towards opening up of retail sector arising from fear of job losses, procurement from international market, compitition and loss of interpreneurials opportunities. However, the government in series of moves has opened up the retail sector slowly to Foreign Direct Investment. In 1997, FDI in cash and carry with 100% ownership was allowed under the government approved route. It was brought under the automatic route in 2006. 51% investment in a single brand retail outlet was aslo permitted in 2006. FDI in Multi-Brand retailing is prohibited in India

MinMax Radon Barcodes for Medical Image Retrieval

Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/her final diagnosis. Beyond conventional feature descriptors, binary features in different ways have been recently proposed to encode the image content. A recent proposal is "Radon barcodes" that employ binarized Radon projections to tag/annotate medical images with content-based binary vectors, called barcodes. In this paper, MinMax Radon barcodes are introduced which are superior to "local thresholding" scheme suggested in the literature. Using IRMA dataset with 14,410 x-ray images from 193 different classes, the advantage of using MinMax Radon barcodes over \emph{thresholded} Radon barcodes are demonstrated. The retrieval error for direct search drops by more than 15\%. As well, SURF, as a well-established non-binary approach, and BRISK, as a recent binary method are examined to compare their results with MinMax Radon barcodes when retrieving images from IRMA dataset. The results demonstrate that MinMax Radon barcodes are faster and more accurate when applied on IRMA images.Comment: To appear in proceedings of the 12th International Symposium on Visual Computing, December 12-14, 2016, Las Vegas, Nevada, US

Integrating Statistical and Machine Learning Approaches to Identify Receptive Field Structure in Neural Populations

Neurons can code for multiple variables simultaneously and neuroscientists are often interested in classifying neurons based on their receptive field properties. Statistical models provide powerful tools for determining the factors influencing neural spiking activity and classifying individual neurons. However, as neural recording technologies have advanced to produce simultaneous spiking data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analyses; however, they typically require massive training sets with balanced data, along with accurate labels to fit well. Additionally, model assessment and interpretation are often more challenging for ML than for classical statistical methods. To address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to demonstrate this framework, we apply these methods to data from a population of neurons recorded from rat hippocampus to characterize the distribution of spatial receptive fields in this region