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This study aims to demonstrate the evaluation of an incentive scheme to encourage pregnant people to set a quit-smoking date.

The paper outlines a collaborative approach, working with pregnant people, clinicians, tobacco dependency practitioners and academics to gain insights into their perspectives and experiences. Quantitative and qualitative data were analysed.

The incentive scheme and appropriate support from clinicians have been shown to encourage pregnant people to set a quit date. The tobacco dependency practitioners helped remove barriers, such as the perception of the stigmatisation of smoking when pregnant. The practitioners also helped pregnant people make informed decisions to support successful behaviour change. The impact of the scheme resulted in improved infant health indicators. The scheme’s evaluation also supported establishing stakeholder knowledge exchange and learning processes.

This is a single-site study among a relatively small group of people designed to achieve a specific evaluation objective. Caution in generalising to wider settings should be exercised.

This study highlights the efficacy of an incentive scheme, complemented with support from clinicians, and the significance of knowledge exchange and collaboration between stakeholders in health care with significance in similar settings.

The paper details the incentive scheme input, actions, output, outcomes and impact involving a wider range of stakeholders, including the emotional consequences for participants, clinicians and academics.

The purpose of this paper is to understand the customer sentiment towards telemedicine apps and also to apply machine learning algorithms to analyse the sentiments in the adoption during the COVID-19 pandemic.

Text mining that uses natural language processing to extract insights from unstructured text is used to find out the customer sentiment towards the telemedicine apps during the COVID-19 pandemic. Machine learning algorithms like support vector machine (SVM) and Naïve Bayes classifier are used for classification, and their sensitivity and specificity are found using a confusion matrix.

The paper explores the customer sentiment towards telemedicine apps and their adoption during the COVID-19 pandemic. Text mining that uses natural language processing to extract insights from unstructured text is used to find out the customer sentiment towards the telemedicine apps during the COVID-19 pandemic. Machine learning algorithms like SVM and Naïve Bayes classifier are used for classification, and their sensitivity and specificity are found using a confusion matrix. The customers who used telemedicine apps have positive sentiment as well as negative sentiment towards the telemedicine apps. Some of the customers have concerns about the medicines delivered, their delivery time, the quality of service and other technical difficulties. Even a small percentage of doctors feel uncomfortable in online consultation through the application.

The primary value of this paper lies in providing an overview of the customers’ approach towards the telemedicine apps, especially during the COVID-19 pandemic.

In mental health care the peer support workers (PSWs) are, by their experience based expertise, supporting the recovery of people using services and have also been shown to stimulate interprofessional learning (IPL) but which, due to hierarchical teams, is challenged. Therefore, to prepare the teams for IPL that includes PSWs, this study aims to suggest an interprofessional education (IPE) for mental health professions and PSWs. What would such an education look like?

The base, in the development of the IPE, is two earlier studies of teams’ inclusion of PSWs and the IPL.

The present study suggests Knowledge base 1 with three categories: different roles, expertise and perspectives, and Knowledge base 2 with two categories: teamwork and IPL. The conclusion is that such online IPE offers a readiness for mental health professions and PSWs, in teamwork, to exchange their different expertise to facilitate IPL. This is important to improve the quality of mental health services.

One limitation is that the empirical study, this paper is based on, is a small-scaled study. Nevertheless, the main results from this study and the other were considered useful as a ground for the development of the IPE.

By suggesting an IPE for mental health professions and PSWs, this paper adds to the literature on peer support as well as IPL.

The purpose of this paper is to explore factors and their impacts influencing online grocery shopping intentions among customers in the post COVID-19 situation. Moreover, the study aims at evaluating the mediating roles of shopping habits during COVID-19 between perceived usefulness, perceived ease of use and post COVID-19 online grocery shopping intentions.

Based on a review of the literature and collection of 401 useable valid responses, the study was conducted through structured questionnaires applying personal interview technique. The subsequent analysis was conducted through partial least squares structural equation modeling (PLS-SEM) using Smart PLS 3.3.3.

The study findings revealed that perceived usefulness, perceived ease of use and shopping habits during COVID-19 have a significant influence on post COVID-19 online grocery shopping intentions. Also, the study has uncovered that perceived usefulness and perceived ease of use significantly influence shopping habits during COVID-19 among customers. Furthermore, the current study has revealed that hopping habit during COVID-19 significantly mediates the relationship between perceived usefulness, perceived ease of use and post COVID-19 online grocery shopping intentions.

The study findings have provided practical suggestions of developing and improving technological platforms to attract new customers for online grocery shopping. Further, the study suggests that online grocery retailers should apply adjusted pricing strategies using coupons and discount offers.

This paper investigates factors and its impacts on online grocery shopping intentions in post COVID-19 context. Therefore, the study uncovers the factors that add value to understanding customers' post COVID-19 online grocery shopping intentions by integrating perceived usefulness, perceived ease of use and shopping habits during COVID-19.

Adolescent pregnancy stands as a societal challenge, compelling young individuals to prematurely discontinue their education. Conversely, an expansion of high school education can potentially diminish rates of adolescent pregnancy, given that educational attainment stands as the foremost risk factor influencing sexual initiation, the use of contraceptive methods during initial sexual encounters and fertility. The aim of this paper is to analyze the impact of the implementation of the public educational policy introducing full-time schools (FTS) for high schools in the state of Ceará, Brazil, on early pregnancy rates.

Using the difference-in-differences method with multiple time periods, we measured the average effect of this staggered treatment on the treated municipalities.

The main result indicates a reduction of 0.849 percentage points in the teenage pregnancy rate. Concerning dynamic effects, the establishment of FTS in treated municipalities results in a 1.183–1.953 percentage point decrease in teenage pregnancy rates, depending on the timing of exposure. We explored heterogeneous effects within socioeconomically vulnerable municipalities, yet discerned no impact on this group. Rigorous tests confirm the robustness of the results.

This paper aims to contribute to: (1) the consolidation of research on the subject, given the absence of such research in Brazil to the best of our knowledge; (2) the advancement and analysis of evidence-based public policy and (3) the utilization of novel longitudinal data and methodology to evaluate adolescent pregnancy rates.

Consumer culture is promoting immediate gratification, and the rise of digital financial services is increasing the risk of indebtedness while debt reduces well-being and affects mental health. The authors assess the effects of consumer information provision, debt literacy, chronic debt and attitudes toward debt on the intent to purchase on credit.

An online survey including an experiment with a credit offer vignette was conducted in a representative sample of Estonia (n = 1204). Treatment conditions depicted either the total cost and duration of the credit agreement or the annual percentage rate.

Receiving modified information resulted in a 26 to 30 percentage points decrease in propensity to purchase on credit. Purchasing on credit was associated with attitudes towards credit and chronic debt, but not with debt literacy.

The findings reveal large effects of information provision and highlight the limited effects of debt literacy on credit decisions. Limitations may emerge from differences in financial regulation across countries.

The authors' results highlight the importance of applying behavioural insights in consumer credit information provision, both in the financial sector and policy. Testing the messages allows having evidence-based solutions that promote responsible purchasing on credit.

The findings call for changes in credit information provision requirements. Their effect is significantly larger compared to the literature, emphasizing the role of credit information provision in less regulated online markets.

No comprehensive statistical assessment of publication bias has been conducted in remdesivir-based intervention research for COVID-19 patients. This study aims to examine all meta-analyses of the efficacy of remdesivir interventions in COVID-19 patients and perform a statistical assessment of publication bias.

This is an analytic study conducted to assess the impact of publication bias on the results of meta-analyses of remdesivir-based interventions in patients infected with COVID-19. All English full-text meta-analyses published in peer-reviewed journals in 2019–2021 were included. A computerized search of PubMed and Web of Science electronic databases was performed on December 24, 2021. The trim-and-fill method calculated the number of missing studies and the adjusted cumulative effect sizes.

The final analysis comprised 21 studies with 88 outcomes. The investigation revealed missing studies in 46 outcomes (52%). Seventy-six missing studies were replaced in the outcomes using the trim-and-fill procedure. The adjusted recalculated effect sizes of the 27 outcomes increased by an average of 0.04. In comparison, the adjusted effect size of 18 outcomes fell by an average of 0.036. Only 14 out of 46 outcomes with publication bias were subjected to a gray literature search (30%). To discover related research, no gray literature search was conducted in most outcomes with publication bias (n = 32; 70%). In conclusion, the reported effect estimates regarding the effect of remdesivir in COVID-19 patients are only slightly affected by publication bias and can be considered authentic. Health-care decision-makers in COVID-19 should consider current research results when making clinical decisions.

Most health decisions are based on the effect sizes revealed in meta-analyses. When deciding on remdesivir-based treatment for COVID-19 patients, therefore, the outcomes of this investigation may be of paramount importance to health policymakers, leading to better treatment strategies.

According to the results, no major publication bias and missing studies were detected on average. Therefore, the calculated effect sizes of remdesivir-based interventions on meta-analyses can be used as authentic and unbiased benchmarks by health-care decision-makers in treating patients with COVID-19.

This is the first study to examine the effect of publication bias and gray literature searches on the results of meta-analyses of treatment with COVID-19 (remdesivir).

Evidence-informed decision-making is considered best practice when choosing interventions in applied settings across health, social care and education. Developing that evidence base, however, is not straightforward. The pupose of this paper is to describe the process implemented by the Sharland Foundation Developmental Disabilities Applied Behavioural Research and Impact Network (SF-DDARIN) that systematically develops an evidence base for behaviorally based interventions.

In this case study, the progressive research steps undertaken by the SF-DDARIN to develop the evidence base for an online reading intervention, the Headsprout® Early Reading programme (HER®), which uses behavioural principles to promote learning to read, are described.

A series of discrete projects targeting gaps in the evidence base for HER^® led to funding two randomised controlled trials in England, one in education and one in health and social care.

This case study illustrates an original, creative and effective way of collaborating across academic research departments and applied settings to extend the evidence base for a chosen intervention systematically.

This paper is a commentary that aims to address themes arising from the article by Morgan titled “Wales and Autism: The Impact of Philanthropy Matched with Ambition”.

This opinion piece considers the broader impact of stakeholder engagement in the field of autism, including how the dissemination of evidence-based support could be made more accessible for autistic people, their families and carers and philanthropists.

This paper highlights the link between stakeholder engagement and meaningful change in policy and practice in the field of autism. It highlights the need for philanthropists to invest in evidence-based support but highlights the difficulty in identifying and accessing such support. This piece supports the use of evidence and gap maps (EGMs) to increase the accessibility of autism research and evidence.

While stakeholder engagement is encouragingly rising in this field, most of the evidence is held in academic journals, which are behind paywalls and, therefore, inaccessible to many autistic people, their families, carers and associated professionals. Displaying evidence using EGMs provides an accessible way for autistic people and those who support them to identify possible avenues of support quickly and allows philanthropists to direct research funding to areas of high priority.

This paper aims to endorse and elaborate on the recommendations put forward by the Sharland Foundation Developmental Disabilities Applied Behavioural Research and Impact Network (SF-DDARIN), emphasising their significance in the field of developmental disabilities.

This paper outlines a specific point of view. The first section focuses on integrating developmental theory and advanced technology in interventions for developmental disabilities. Subsequently, the commentary explores virtual reality (VR) and generative artificial intelligence (AI) for enhancing social skills and personalising support. Finally, the piece highlights innovations like SocialWise VR and Custom Generative Pre-Trained Transformers in aligning interventions with developmental stages.

Technologies like VR and generative AI hold vast potential to revolutionise how clinicians provide timely and relevant knowledge to individuals with developmental disabilities.

This is strictly a commentary.

Availability of technology.

Both VR and generative AI will impact service delivery in a meaningful way.

The paper advocates for incorporating these technologies into SF-DDARIN's approach, emphasising their potential to revolutionise evidence-based interventions in developmental disabilities.