• Decoding Organisational Attractiveness: A Fuzzy Multi-Criteria Decision-Making Approach

  Roodbari H; Rahimi R; Oraee A; Vatankhah, Sanaz; roodbari; University of Bedfordshire (Emerald, 2024-09-16)

  Purpose- High-skilled employees are crucial for sustained competitive advantage of organisations. In the "war for talent", organisations must position themselves as attractive employers. This study introduces a unified framework to systematically identify and prioritise Organisational Attractiveness (OA) components, focusing on the extreme context of the airline industry. Design/methodology/approach- Treating OA as a Multi-Criteria Decision Making (MCDM) situation, the study employs the Fuzzy Delphi Method (FDM) to validate key OA factors and the Fuzzy Analytical Hierarchy Process (FAHP) to prioritise them based on experts’ judgements. Findings- The study identifies five criteria and 22 sub-criteria for OA, with job characteristics and person-job fit as most critical. These elements signal employment quality and skill-job alignment, reducing information asymmetry and attracting talent. Practical implications- This research provides a practical framework for airline managers to identify and prioritise key aspects of OA to enhance their value proposition and attract and retain qualified employees. For policymakers, applying the OA framework supports informed policy decisions on employment standards and workforce development. Originality- This research introduces a fuzzy OA index and a framework that enhances OA. By incorporating signalling theory into a fuzzy MCDM approach, it systematically addresses key OA components, offering a strategic method to boost OA.
• Ten tips when building a centralised evaluation unit

  Briggs, Steven G.; Pritchard, Diana J.; Kaya, Sibel; Sidaway, Kathryn; Brunton, Julie; University of Bedfordshire (Times, 2024-08-01)

  How can we establish and develop evaluation activities to show what support and interventions affect the student experience, as well as their learning, outcomes and destinations? Universities increasingly need to demonstrate their practices are based on evidence. They face demands from external regulators to show the impact of educational and other institutional practices on the student experience, learning outcomes and graduate destinations. Many higher education institutions will need to change and refine their evaluation processes and approaches to meet these demands. Here, we outline 10 tips for establishing a centralised evaluation unit, to lead on institutional evaluation and support evaluation activities, within your institution.
• Health and social care professionals experience, views and responses toward people who use new psychoactive substances in different mental health and addiction services.

  Solomon, David; Grierson, Jeffrey; Godier-McBard, Lauren; Guirguis, Amira (2024-09-13)

  Title: Healthcare professional’s experiences, views and responses of People Who Use New Psychoactive Substances in different Mental Health and Addiction Healthcare Services. Authors: Dr David Solomon, Professor Jeffrey Grierson, Associate Professor Lauren Godier-McBard, Associate Professor Amira Guirguis. Background New psychoactive Substances (NPS) cause harm to both the physical and mental health of people who use NPS (PWUNPS). Health and social care professionals working in mental health and drug and alcohol settings experience daily challenges surrounding the identification of NPS types, and related symptoms resulting from NPS. Although a limitation of research exists surrounding how Hcps manage PWUNPS, more research is needed on Hcps views, responses, and experiences across different healthcare services (HCSs) surrounding their engagements with PWUNPS. Aim (s) Exploring the experiences, views, and responses of health and social care professionals in contact with people who use new psychoactive substances. Sampling Method: Purposive sampling Methods: A Sequential Explanatory Design was carried out in three different service type provisions namely statutory, non-statutory, and private sectors across five mental health and drug and alcohol HCSs. Specific Analytical approach: Descriptive Statistics and Thematic Analysis. Main Findings In total, 92 Hcps took part in the survey across five different HCSs. Most Hcps were female (n=47) in comparison to male Hcps (n=3) and some Hcps (n=2) did not disclose. 45% of Hcps reported no assessment or procedures were in place for PWUNPS and views were predominately neutral towards PWUNPS and engagement experiences were deemed neutral. The phase 2, (n=14 ) semi-structured interviews results identified 5 common themes associated with Hcps experiences and responses toward PWUNPS including organisational issues, assessment, stigma, harm minimisation and a symptoms as contributing factors toward Hcps experiences surrounding the management of PWUNPS. Discussion/ Conclusions Hcps frequently meet PWUNPS across different HCSs presenting with various health-related co-morbidity. Organisational issues impacted the engagements, access and funding toward treating PWUNPS. HCSs need to integrate specific NPS trainings for Hcps across the different HCS sectors is recommended to reduce the harms associated with NPS use. This study demonstrates the potential of implementing newer assessment, policy, and a Harm Minimisation approach toward PWUNPS across different HCSs.
• Addressing mental health and addictions among undergraduate nursing students in New Zealand

  Solomon, Bernadette; Topp, M.; Solomon, David; Solomon, D.J.A. (2024-09-13)

  Abstract Background Undergraduate nursing programs often mirror the general population in terms of mental health challenges and substance misuse. Some studies even indicate higher rates of these issues among nursing students. However, this area currently remains under-researched in New Zealand. Study Details: * Participants: The study involved 172 nursing students enrolled in the Bachelor of Nursing program at Manukau institute of Technology, New Zealand. * Methodology: A mixed-methodology design was used combing a 29- question survey with both closed and open- ended questions. The survey explored students’ personal experiences related to mental health and substance misuse. Key Findings: * Emotional Distress. A significant 75% of students reported anxiety * Substance Misuse: A small proportion (8.1%) reported substance misuse. * Self-Harm and Suicidal Ideation: Approximately 22.1% expressed thoughts of self-harm, and 19.4% reported suicidal ideation. * Support Sources: Students primarily relied on self-management strategies (40.1%) or family/friend support (41.9%). Institutional support services were underutilised (less than 1%) often due to lack of availability or awareness. Implications: The study highlights the need for institutional change: * Accessible Support: Institutions must provide accessible and personalised support to nursing students. * Academic Success: Addressing emotional distress is crucial for students’ academic success. * Nursing Workforce Well-Being: Supporting students contributes to the well-being of the nursing workforce. The findings highlight the urgent need to address the mental health and addiction challenges experienced by nursing students, given their potential adverse effects on academic success and overall well-being. Urgent action is needed to integrate mental health training into the curriculum and supporting faculty.The underutilisation and inadequacy of institutional support services signal a need for institutional reforms to provide access and personalised mental health support to nursing students. Providing essential skills and support for student success contributes to the overall well-being of the nursing workforce.
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  DeepDetect: an innovative hybrid deep learning framework for anomaly detection in IoT networks

  Zulfiqar, Zeenat; Malik, Saif U.R.; Moqurrab, Syed Atif; Yaseen, Usman; Zulfiqar, Zubair; Srivastava, Gautam; COMSATS University; Information Security Institute, Estonia; University of Bedfordshire; National University of Sciences and Technology (NUST), Pakistan; et al. (Elsevier B.V., 2024-09-06)

  The presence of threats and anomalies in the Internet of Things infrastructure is a rising concern. Attacks, such as Denial of Service, User to Root, Probing, and Malicious operations can lead to the failure of an Internet of Things system. Traditional machine learning methods rely entirely on feature engineering availability to determine which data features will be considered by the model and contribute to its training and classification and “dimensionality” reduction techniques to find the most optimal correlation between data points that influence the outcome. The performance of the model mostly depends on the features that are used. This reliance on feature engineering and its effects on the model performance has been demonstrated from the perspective of the Internet of Things intrusion detection system. Unfortunately, given the risks associated with the Internet of Things intrusion, feature selection considerations are quite complicated due to the subjective complexity. Each feature has its benefits and drawbacks depending on which features are selected. Deep structured learning is a subcategory of machine learning. It realizes features inevitably out of raw data as it has a deep structure that contains multiple hidden layers. However, deep learning models such as recurrent neural networks can capture arbitrary-length dependencies, which are difficult to handle and train. However, it is suffering from exploiting and vanishing gradient problems. On the other hand, the log-cosh conditional variational Autoencoder ignores the detection of the multiple class classification problem, and it has a high level of false alarms and a not high detection accuracy. Moreover, the Autoencoder ignores to detect multi-class classification. Furthermore, there is evidence that a single convolutional neural network cannot fully exploit the rich information in network traffic. To deal with the challenges, this research proposed a novel approach for network anomaly detection. The proposed model consists of multiple convolutional neural networks, gate-recurrent units, and a bi-directional-long-short-term memory network. The proposed model employs multiple convolution neural networks to grasp spatial features from the spatial dimension through network traffic. Furthermore, gate recurrent units overwhelm the problem of gradient disappearing- and effectively capture the correlation between the features. In addition, the bi-directional-long short-term memory network approach was used. This layer benefits from preserving the historical context for a long time and extracting temporal features from backward and forward network traffic data. The proposed hybrid model improves network traffic's accuracy and detection rate while lowering the false positive rate. The proposed model is evaluated and tested on the intrusion detection benchmark NSL-KDD dataset. Our proposed model outperforms other methods, as evidenced by the experimental results. The overall accuracy of the proposed model for multi-class classification is 99.31% and binary-class classification is 99.12%.

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