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This study aims to examine the relationship between medication for opioid use disorder (MOUD) among pregnant individuals, referral source, mental health, political affiliation and substance use policies in all 50 states in the USA.

This study describes MOUD receipt among pregnant people with an opioid use disorder (OUD) in 2018. The authors explored sociodemographic differences in MOUD receipt, referrals and co-occurring mental health disorders. The authors included a comparison of MOUD receipt among states that have varying substance use policies and examined the impact of these policies and the political affiliation on MOUD. The authors used multilevel binary logistic regression to examine effects of individual and state-level characteristics on MOUD.

Among 8,790 pregnant admissions with OUD, the majority who received MOUD occurred in the Northeast region (71.52%), and 14.99% were referred by the criminal justice system (n = 1,318). Of those who were self-referred, 66.39% received MOUD, while only 30.8% of referrals from the criminal justice system received MOUD. Those referred from the criminal justice system or who had a co-occurring mental health disorder were least likely to receive MOUD. The multilevel model showed that while policies were not a significant predictor, a state’s political affiliation was a significant predictor of MOUD.

The study has some methodological limitations; a state-level analysis, even when considering the individual factors, may not provide sufficient description of community-level or other social factors that may influence MOUD receipt. This study adds to the growing literature on the ineffectiveness of prenatal substance use policies designed specifically to increase the use of MOUD. If such policies are consistently assessed as not contributing to substantial increase in MOUD among pregnant women over time, it is imperative to investigate potential mechanisms in these policies that may not facilitate MOUD access the way they are intended to.

Findings from this study aid in understanding the impact that a political affiliation may have on treatment access; states that leaned more Democratic were more likely to have higher rates of MOUD, and this finding can lead to research that focuses on how and why this contributes to greater treatment utilization. This study provides estimates of underutilization at a state level and the mechanisms that act as barriers, which is a stronger assessment of how state-specific policies and practices are performing in addressing prenatal substance use and a necessary step in implementing changes that can improve the links between pregnant women and MOUD.

To the best of the authors’ knowledge, this is the first study to explore individual-level factors that include mental health and referral sources to treatment that lead to MOUD use in the context of state-level policy and political environments. Most studies estimate national-level rates of treatment use only, which can be useful, but what is necessary is to understand what mechanisms are at work that vary by state. This study also found that while substance use policies were designed to increase MOUD for pregnant women, this was not as prominent a predictor as other factors, like mental health, being referred from the criminal justice system, and living in a state with more Democratic-leaning affiliations.

This study aims to investigate the prevalence and characteristics of dysphagia among adults with intellectual disabilities (ID) referred to speech and language therapy for swallow assessment, providing information on the demographic characteristics, referral trends, co-occurring health conditions and reasons for referrals highlighting the complex health-care needs of this population.

This study used a standardised patient data extraction method over a six-month period involving 74 adults with ID referred to speech and language therapy for swallow assessment.

This study revealed a high prevalence of dysphagia among adults with ID referred to speech and language therapy for swallow assessment. Increasing age and severity of ID were associated with an increased likelihood of swallowing difficulties. Co-occurring health conditions such as mobility difficulties, epilepsy and gastrointestinal conditions were prevalent, suggesting that adults with ID and swallowing difficulties are often living with complex health conditions. Choking incidents and hospital admissions were primary reasons for referral.

This study stresses the pressing need for strategies to mitigate risks associated with choking incidents and hospital admission among this vulnerable population. Possible limitations include a reliance on referral and the focus being on a single service over a short period which may limit generalisation to the wider ID population.

This study emphasises the need to understand each person’s unique profile of health needs and the value of a specialised speech and language therapy service.

The importance of increasing awareness among caregivers and medical experts is highlighted.

The findings underscore the importance of tailored assessment, caregiver involvement and heightened interdisciplinary awareness to effectively manage dysphagia in individuals with ID.

The circular economy business models (CEBMs) provide ways for firms operating in the construction industry to move from a linear to a circular approach. Thus, this study aims to explore CEBM research within the construction sector to show the focus area of studies, highlighting new areas that require attention.

This study adopted a bibliometric approach, using the Scopus database as the data source. The keywords used for paper extraction from the database were “circular economy business” OR “circular business” AND “model” OR “models” AND “construction industry” OR “building industry”. The VOSviewer software was then used to prepare a co-occurrence and co-authorship map based on the bibliographic data gathered.

The study’s findings reveal five research clusters in the construction industry. These clusters include circular construction intelligence, modular business modelling, eco-construction, sustainable construction economics and smart energy-efficient buildings. The two most cited scholars had two publications each, while the top journals are the Journal of Cleaner Production and Sustainable Production and Consumption. This study concludes that there is a need for research within the construction sector to focus on CEBMs’ archetypes and frameworks. This will enable a smooth transition from linear to circular business models in the sector.

The information was gathered from a single database, Scopus; hence, using other databases, including Web of Science, Google Scholar and Dimensions, might produce more articles for examination and, consequently, different findings on the subject under investigation.

These findings would assist researchers in considering the areas mentioned, which are yet to receive attention, and, by extension, enhance economic development while maintaining environmental sustainability.

This paper made a significant contribution to the body of knowledge by identifying scholars and platforms that have been instrumental in advancing CEBM research and highlighting new areas that require attention in the construction sector.

Non-verbal communication (NVC) remains largely understudied despite its importance in today's fast-paced and cross-cultural management and research landscape. This article is significant because it reveals valuable insights into NVC, which represents 65–93% (Mehrabian, 1981) of communication and has the potential to considerably increase management effectiveness and efficiency by providing leaders and researchers with the knowledge they need to understand and handle diversity with competence.

This article draws on social identity theory (SIT) (Tajfel and Turner, 1979) and rapport management theory (RMT) (Brown and Levinson, 1987) to analyse illustrative interview extracts of co-occurring verbal and NVC from an interpretative phenomenological analysis (IPA) study focussed on understanding how London-based Romanian migrant entrepreneurs experience acculturation.

Romanian migrant entrepreneurs use a variety of verbal and non-verbal communication approaches in their acculturation narratives, providing depth and occasionally shifting meaning. These tactics include repeating verbal discourse with non-verbal clues, replacing verbal communication with non-verbal gestures, complementing verbal communication and juxtaposing non-verbal cues with verbal descriptions.

This study makes a valuable contribution to the fields of qualitative organisational management and entrepreneurial studies by addressing the lack of methodological tools available for analysing non-verbal language in interpretative research. This study presents a systematic technique for assessing non-verbal language symbols that has been developed through face-to-face interviews. The article utilises the first-hand interview experience of a Romanian co-researcher to demonstrate the significance of NVC in the transmission of meaning and the formation of identities amongst Romanian migrant entrepreneurs. These findings contribute to a better understanding of organisational management and research practices, particularly about this understudied entrepreneurial minority of Romanian businesses in London, by helping researchers and managers better grasp the cultural and contextual meanings communicated non-verbally. The article holds significance in the context of cross-cultural and organisational management practices.

The study aims to explore the overall growth trend, top publishing countries, co-authorship and author keywords in the field of Industry 5.0.

This study presents the outcomes of a bibliometric analysis conducted using VOSviewer software. The analysis retrieved data from the Scopus database, including citations, co-authors, keywords, bibliometric coupling and co-occurrence.

The findings reveal a significant increase in publications and citations related to Industry 5.0 in recent years. China, the USA and India emerge as the leading countries driving research in this field. The co-authorship analysis indicates limited collaboration among authors, with only 48 out of 354 authors being linked through co-authorship. Through co-occurrence analysis, the investigation identifies the most frequently occurring keywords in the research, with “Industry 5.0” and “Industry 4.0” being the most frequently co-occurring keywords. The bibliographic coupling analysis identifies six clusters of research themes.

The study solely relies on data gathered from the Scopus database for analysis on a specific date. Therefore, data from other databases collected at different times may yield different observations and findings.

This study enhances the knowledge of professionals and academia in Industry 5.0, enabling the professionals to efficiently and sustainably manage the sector.

The bibliometric analysis presented in this study provides valuable insights into the contributions made by authors, keywords and co-authors to the field of Industry 5.0. Additionally, the thematic analysis summarized in this study is a novel contribution to the field.

The increasing urgency to address climate change in construction has made green construction (GC) and sustainability critical topics for academia and industry professionals. However, the volume of literature in this field has made it impractical to rely solely on traditional systematic evidence mapping methodologies.

This study employs machine learning (ML) techniques to analyze the extensive evidence-base on GC. Using both supervised and unsupervised ML, 5,462 relevant papers were filtered from 10,739 studies published from 2010 to 2022, retrieved from the Scopus and Web of Science databases.

Key themes in GC encompass green building materials, construction techniques, assessment methodologies and management practices. GC assessment and techniques were prominent, while management requires more research. The results from prevalence of topics and heatmaps revealed important patterns and interconnections, emphasizing the prominent role of materials as major contributors to the construction sector. Consistency of the results with VOSviewer analysis further validated the findings, demonstrating the robustness of the review approach.

Unlike other reviews focusing only on specific aspects of GC, use of ML techniques to review a large pool of literature provided a holistic understanding of the research landscape. It sets a precedent by demonstrating the effectiveness of ML techniques in addressing the challenge of analyzing a large body of literature. By showcasing the connections between various facets of GC and identifying research gaps, this research aids in guiding future initiatives in the field.

The resource crisis in healthcare can be alleviated by engaging external stakeholders and resources in healthcare delivery. The authors use value and open innovation concepts to understand what motivates the stakeholders to join the healthcare innovation ecosystem and what value such an ecosystem brings to healthcare.

A systematic literature review following the PRISMA framework method was applied to reach the research objective. Out of a total of 509 identified articles published till 2021, 25 were selected as relevant for this review.

Six categories of actors were identified, including innovation intermediaries, which were so far neglected in the healthcare innovation literature. Furthermore, patients, healthcare providers, innovation suppliers, investors and influencers were described. The authors also distinguished internal and external stakeholders. The authors show why and how open innovation projects contribute to involving external stakeholders and resources in healthcare delivery by contributing to patient autonomy, relationship building, knowledge transfer, improving collaborative mindset and culture, advancing know-how and bringing additional finances.

This article is the first one to systematically describe the value of open innovation in healthcare. The authors challenge the positivist approach in value presented by value-based healthcare. The authors show how openness contributes to addressing the resource crisis by involving new stakeholders and resources in the care delivery process.

The most recent wave of the opioid epidemic in the USA has been complicated by a sharp increase in methamphetamine use. In addition, many people classified with opioid use disorder (OUD) and methamphetamine use disorder (MUD) present indications of psychiatric conditions. These diagnoses are also highly prevalent among people who are admitted to jails, but research conducted with this population is limited, due in part to the challenges associated with gaining access to local detention centers. This paper aims to examine the patterns of psychiatric conditions, OUD, and MUD among an understudied population to help inform the development of service delivery systems.

The present study was designed to assess the prevalence of OUD, MUD and common psychiatric conditions in a large sample of adults (n = 846) collected from four local jails. Diagnostic patterns were evaluated according to the current criteria established in the Diagnostic and Statistical Manual of Mental Disorders (5th ed; American Psychiatric Association, 2013).

More than half (57.3%) of the sample met criteria for MUD, one-third (37.2%) exceeded the threshold for an OUD diagnosis and 15.7% were classified with both conditions. Participants who met criteria for both MUD and OUD were significantly more likely to experience symptoms of major depression [adjusted odd ratios (aOR) = 1.76, 9, confidence intervals (CI) = 1.16–2.67], post-traumatic stress disorder (aOR = 2.51, 1.64–3.83), panic attacks (aOR = 3.24, 95% CI = 2.05–5.13), obsessive compulsive disorder (aOR = 2.74, 95% CI = 1.66–4.51) and antisocial personality (aOR = 3.03, 95% CI = 1.97–4.64).

These results, which were derived from an understudied population of adults detained in local jails, indicate the co-–occurrence of MUD and OUD are associated with certain psychiatric conditions.

Employing the “Job demands-resources (JD-R)” framework, this study examines the impact of co-occurring social supports (supervisor, coworker, and family support) on the telework environment and employee engagement.

The study uses a multimethod approach. Data from 294 employees belonging to Indian technology organizations were collected and analyzed using the partial least squares (PLS)-based structure equation modeling software SmartPLS4. Following this, necessary condition analysis (NCA) was carried out using the NCA package for R.

Telework environment was found to mediate the relationship between social support and work engagement. Supervisor support and instrumental family support were identified as predictors as well as necessary conditions for telework environment. Coworker support was identified both as a predictor and necessary condition for telework environment. Although emotional family support was found to be a predictor of telework environment, it was not identified as a necessary condition.

The findings indicate that coworker support and family instrumental support are as important for telework success as supervisor support. Moreover, our findings suggest that varying levels of telework environments (low, moderate, and high) may necessitate distinct social support configurations. Consequently, organizations should match their social support configuration to match their overall teleworking strategy.

A basic premise of the JD-R framework is that resources exist in caravans (bundles). However, previous research (in telework) has concentrated on only one or two kinds of social support, that too in varying situational contexts, limiting generalizability of the findings. This has also produced inconsistent conclusions concerning the role of support providers such as coworkers and family. Recent developments in JD-R also suggest that the role of resources may vary in terms of their importance (necessity) for work engagement. By augmenting standard regression-based techniques with NCA, the authors explore these issues to provide a more thorough understanding of the influence of social supports on work engagement in telework situations.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.