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The outbreak of COVID-19 not only had serious negative impacts on the world economy but also on the global mental health because of the psychological disorders associated with the spread of the pandemic, the increased degree of uncertainty and the unprecedented measures taken by different countries to face the pandemic’s spread. This paper analyses the mental health well-being of individuals in selected MENA countries (Jordan, Morocco, Tunisia and Egypt) during the pandemic.

The study employs a pooled OLS model using the Economic Research Forum (ERF) COVID-19 MENA Monitor Survey panel dataset collected during 2020 and 2021.

The findings show that there is no association between the mental health of individuals in the selected countries and their age, gender, family size, marital status, receipt of social support and participation in care work. Mental health improved at higher levels of education, being employed, being a rural area resident and living in Morocco or Tunisia compared to living in Jordan while it worsened as income declined, food insecurity and anxiety about being infected with Covid-19 increased, being a resident in camps, and during waves 4 and 5. Based on these results, it is recommended that suitable financial, physical and human resources should be directed towards the provision of mental health care services in the region. Also, mental health care services should be accessible to different population groups, with a special focus towards the most vulnerable since they are more prone to mental illnesses, especially during health crises and economic shocks. This should be accompanied by increasing awareness about the provided services and reducing stigma against mental illnesses. Furthermore, introduction of policies targeted towards reducing food insecurity and income instability can play a key role in enhancing mental well-being.

Although few papers have previously investigated the impact of COVID-19 on mental health in MENA countries, most of them have focused on a country-level analysis and adopted a gender perspective. Hence, this paper aims at exploring the association between mental health well-being and socio-economic factors in selected MENA countries during the pandemic.

This paper aims to reflect on the outcomes of a community-based video-conferencing intervention for depression, predating the COVID-19 pandemic. The study investigates the potential implications of its findings for enhancing adherence to digital mental health interventions. The primary objective is to present considerations for researchers aimed at minimising the intention-behaviour gap frequently encountered in digital mental health interventions.

A randomised control feasibility trial design was used to implement a telehealth model adapted from an established face-to-face community-based intervention for individuals clinically diagnosed with depression. In total, 60 participants were initially recruited in association with a local mental health charity offering traditional talking-based therapies with only eight opting to continue through all phases of the project. Modifications aligning with technological advancements were introduced.

However, the study faced challenges, with low uptake observed after an initial surge in recruitment interest. The behaviour-intention gap highlighted technology as a barrier to service accessibility, exacerbated by participant age. Furthermore, the clinical diagnosis of depression, characterised by low mood and reduced interest in activities, emerged as a potential influencing factor.

The limitations of the research include its pre-pandemic execution, during a nascent stage of technological mental health interventions when participants were less familiar with online developments.

Despite these limitations, this study's reflections offer valuable insights for researchers aiming to design and implement telehealth services. Addressing the intention-behaviour gap necessitates a nuanced understanding of participant demographics, diagnosis and technological familiarity.

The study's relevance extends to post-pandemic society, urging researchers to reassess assumptions about technology availability to ensure engagement. This paper contributes to the mental health research landscape by raising awareness of critical considerations in the design and implementation of digital mental health interventions.

Reflections from a pre-pandemic intervention in line with the developments of a post-pandemic society will allow for research to consider that because the technology is available does not necessarily result in engagement.

The purpose of this paper is to understand how hospitality companies can best explain controversial corporate social responsibility (CSR) activities to consumers who may not agree with the CSR activity. This research explores message framing through emotional and cognitive appeals to influence consumer perceptions of the Gideon Bible in USA hotel rooms. The study uses the theory of deontic justice to measure the impacts of messaging on consumer perceptions of the morality of the Gideon Bible as suicide prevention in hotels and its relation to controversial CSR initiatives.

The study uses an experimental study design via a self-administered survey to analyze participants’ perceptions of the placement of the Gideon Bible in hotel rooms and participants’ attitudes toward CSR initiatives based on deontic justice and religion using different message framing conditions.

Results show that religion was a major determinant of attitude towards the Gideon Bible, but the sentiment analysis also revealed that negative perceptions can be mitigated through message framing via emotional and cognitive appeals. Additionally, the cognitive appeal did impact CSR perceptions, as did identifying as Christian. Moral outrage emerged as a significant moderator for the relationships between message framing, attitudes toward the Gideon Bible and CSR.

This study provides an extension of deontic justice research to examine justice traits in accepting controversial CSR.

Although microfinancing is considered a key tool for fostering women's entrepreneurship development, there is growing concern regarding the impact of loan repayment strategies on the mental health of women entrepreneurs. This study seeks to unravel the implications of microfinance loans on the mental well-being of women entrepreneurs.

A carefully structured questionnaire was distributed to a purposive sample of one hundred women entrepreneurs in Lagos State using a mixed-method research approach. Also, interviews were conducted using an interview guide, which directly mirrored the questionnaire administered to five focus groups within Lagos State.

The study found that loan repayment and recovery strategies positively and significantly impact women entrepreneurs' mental well-being, with psychological distress serving as a measure for measuring mental well-being. Additionally, other factors such as the number of dependants and household headship trigger psychological distress, while age in business was associated with a reduction in psychological distress.

This study contributes to the existing literature by delving into the psychological implications of loan repayment strategies on the mental health of female entrepreneurs in Lagos State, Nigeria. Furthermore, it employs a triangulation research approach to validate questionnaire responses through focus group discussions.

This paper aims to propose a novel way of using textual clustering as a feature selection method. It is applied to identify the most important keywords in the profile classification. The method is demonstrated through the problem of sick-leave promoters on Twitter.

Four machine learning classifiers were used on a total of 35,578 tweets posted on Twitter. The data were manually labeled into two categories: promoter and nonpromoter. Classification performance was compared when the proposed clustering feature selection approach and the standard feature selection were applied.

Radom forest achieved the highest accuracy of 95.91% higher than similar work compared. Furthermore, using clustering as a feature selection method improved the Sensitivity of the model from 73.83% to 98.79%. Sensitivity (recall) is the most important measure of classifier performance when detecting promoters’ accounts that have spam-like behavior.

The method applied is novel, more testing is needed in other datasets before generalizing its results.

The model applied can be used by Saudi authorities to report on the accounts that sell sick-leaves online.

The research is proposing a new way textual clustering can be used in feature selection.

Drawing on the natural resource-based view (NRBV) theory, the study aimed to examine the mediating role of green innovation (GI) in the nexus between green human resource management practices (Green HRMPs) and sustainable business performance (SBP).

This study adopted the descriptive time-lagged research design. Data were collected from 278 managerial staff of five mining companies in Ghana at different waves within a 3-month interval. Descriptive and inferential statistics were used to analyse the data received using the statistical package for the social sciences (SPSS) statistics (V. 26.0) and Smart PLS (V.4.0).

The study found that Green HRMPs significantly related more to economic performance (EP) than social performance (SP) but did not significantly relate to environmental performance (EnP). Moreover, the results revealed that GI partially mediated the nexus between Green HRMPs and both SP and EP but fully mediated the link between Green HRMPs and EnP.

The relevance of Green HRMPs in ensuring corporate sustainability has been largely established in the extant literature. However, there is an evidential dearth of studies in the literature concerning the mediating role of GI in the nexus between Green HRMPs and SBP, especially in developing economies context. Hence, this study serves as a significant contributing card from Ghana by advancing the NRBV theory.

Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.

The aim of this paper is to describe and detail a new workforce initiative developed by a third sector organisation: the Multi-Modality Practitioner (MMP) approach, in the provision of support for individuals and families presenting in the community with multiple and varied needs.

This paper presents a case study on a third sector organisation’s pioneering workforce initiative – the MMP approach.

The MMP approach has been developed as an alternative to existing approaches designed to address “single issues”, providing those working in the fields of health and social care and beyond, with a range of technically eclectic interventions from different modalities and approaches, to enable them to appropriately meet the needs of individuals and families presenting with multiple needs.

This case study is limited to providing a rationale for the development of the MMP approach and a description. It does not include any formal analysis as a future evaluative paper will report on an independent theory-based examination of the MMP approach via Theory of Change and Contribution Analysis methodologies.

This case study describes the innovative role that the MMP approach as a workforce innovation plays in meeting multiple needs in the community when deployed as either a stand-alone approach, or one that can be integrated with existing community-based services including, but not limited to, mental health, employability, addiction, housing, homelessness, public health and child and family services.

The MMP approach constitutes a disruptive, pionneering, client-centred, workplace innovation that is trauma-informed, relational, strengths-based and continuity of care focused, capable of supporting individuals and families with multiple needs, negating onward referral and with potential to reduce demand on public services. It also provides those working in a range of health and social care roles with the opportunity to expand their therapeutic repertoire through high-quality, multi-skilled training in evidence-based and societally important, technically eclectic interventions drawn from a range of modalities.

Pluralistic practice and multimodal theories have been in existence for over two decades, the MMP approach however, whilst sharing some of these concepts and philosophies, differs in that it provides those trained in the approach with a ‘toolkit’ of interventions that are technically eclectic, drawn from a range of modalities and approaches and aimed at meeting multiple needs in a pragmatic and accessible manner. Furthermore, the Diploma in Multi-Modality Practice is the first training course of its kind available at postgraduate level.

Clinical staff working in mental health services experience high levels of work-related stress, burnout and poor well-being. Increased levels of stress, burnout, depression and anxiety and poorer mental well-being among health-care workers are associated with more sick days, absenteeism, lower work satisfaction, increased staff turnover and reduced quality of patient care. Virtual reality (VR) relaxation is a technique whereby experiences of pleasant and calming environments are accessed through a head-mounted display to promote relaxation. The purpose of this paper is to describe the design of a study that assesses the feasibility and acceptability of implementing a multi-session VR relaxation intervention amongst mental health professionals, to improve their relaxation levels and mental well-being.

The study follows a pre–post-test design. Mental health staff will be recruited for five weeks of VR relaxation. The authors will measure the feasibility and acceptability of the VR relaxation intervention as primary outcomes, alongside secondary outcomes evaluating the benefits of VR relaxation for mental well-being.

The study aims to recruit 20–25 health-care professionals working in both inpatient and specialist community mental health settings.

Research indicates the potential of VR relaxation as a low-intensity intervention to promote relaxation and reduce stress in the workplace. If VR relaxation is shown to be feasible and acceptable, when delivered across multiple sessions, there would be scope for large-scale work to investigate its effectiveness as an approach to enable health-care professionals to de-stress, relax and optimise their mental well-being. In turn, this may consequently reduce turnover and improve stress-related sick leave across health-care services.