mHealth interventions for postpartum family planning in LMICs: A realist review

The unmet need for family planning is a pervasive public health concern in many low- and middle-income countries (LMICs). Mobile health (mHealth) interventions have been designed and implemented in LMIC settings to address this issue through health information dissemination via voice calls, apps, and short message services (SMS). Although the impact of mHealth programmes on postpartum family planning outcomes have been systematically reviewed, the contexts, conditions, and mechanisms underpinning programme engagement and their impact on outcomes remain unclear. This study aims to formulate hypotheses in the form of context-mechanism-outcome configurations (CMOCs) of whether, how, why, for whom, and in what contexts mHealth interventions implemented in LMICs influence postpartum family planning (PPFP) outcomes. We conducted a realist review of peer-reviewed and grey literature. Peer-reviewed literature was identified through MEDLINE, Embase, Global Health, Web of Science, and Google Scholar. Grey Literature was identified through The National Grey Literature Conference, FHI 360, Guttmacher Institute, Population Council, and MSI Reproductive Choices. Inclusion criteria were updated as the review progressed. Narrative data were analysed using dimensional analysis to build CMOCs. Two overarching concepts (underpinned by 12 CMOCs) emerged from the 37 included records: mobile phone access, use, and ownership as well as women’s motivation. Women’s confidence to independently own, access, and operate a mobile phone was a central mechanism leading to mHealth programme engagement and subsequent change in PPFP knowledge, awareness, and outcomes. Receiving family and social support positively interacted with this while low digital literacy and harmful gender norms pertaining to prescribed domestic duties and women’s household influence were barriers to programme engagement. Intrinsic motivation for health improvement functioned at times both as a context and potential mechanism influencing mHealth programme engagement and PPFP outcomes. However, these contexts rarely occur in isolation and need to be evaluated as co-occurring phenomena. (Review registration: PROSPERO CRD42023386841)

mHealth interventions for postpartum family planning in LMICs: A realist review.

The unmet need for family planning is a pervasive public health concern in many low- and middle-income countries (LMICs). Mobile health (mHealth) interventions have been designed and implemented in LMIC settings to address this issue through health information dissemination via voice calls, apps, and short message services (SMS). Although the impact of mHealth programmes on postpartum family planning outcomes have been systematically reviewed, the contexts, conditions, and mechanisms underpinning programme engagement and their impact on outcomes remain unclear. This study aims to formulate hypotheses in the form of context-mechanism-outcome configurations (CMOCs) of whether, how, why, for whom, and in what contexts mHealth interventions implemented in LMICs influence postpartum family planning (PPFP) outcomes. We conducted a realist review of peer-reviewed and grey literature. Peer-reviewed literature was identified through MEDLINE, Embase, Global Health, Web of Science, and Google Scholar. Grey Literature was identified through The National Grey Literature Conference, FHI 360, Guttmacher Institute, Population Council, and MSI Reproductive Choices. Inclusion criteria were updated as the review progressed. Narrative data were analysed using dimensional analysis to build CMOCs. Two overarching concepts (underpinned by 12 CMOCs) emerged from the 37 included records: mobile phone access, use, and ownership as well as women's motivation. Women's confidence to independently own, access, and operate a mobile phone was a central mechanism leading to mHealth programme engagement and subsequent change in PPFP knowledge, awareness, and outcomes. Receiving family and social support positively interacted with this while low digital literacy and harmful gender norms pertaining to prescribed domestic duties and women's household influence were barriers to programme engagement. Intrinsic motivation for health improvement functioned at times both as a context and potential mechanism influencing mHealth programme engagement and PPFP outcomes. However, these contexts rarely occur in isolation and need to be evaluated as co-occurring phenomena. (Review registration: PROSPERO CRD42023386841)

7 The LISTEN method – synthesising collaborative and digital methods for big qualitative data analysis

Background: Big qualitative data analysis is an emerging discipline in qualitative health research and has been used with online posts, open-ended survey responses, and patient health records. Traditional methods of qualitative data analysis can be time-consuming and biased by small sample sizes. The combined strengths of collaborative and participatory methods from rapid research approaches and the efficiency of digital software analyses can mitigate these issues. Aim: We developed the LISTEN method (Collaborative and Digital Analysis of Big Qualitative Data in Time Sensitive Contexts), combining interdisciplinary expertise in collaborative, participatory, and digital methods for big qualitative data analysis. Methods: The LISTEN project iteratively combines findings from a systematic review of peer-reviewed literature and world-wide-web data as well as consultation with stakeholders, collaborative team discussions and text network analysis using digital software. Text and thematic analysis software was used to conduct sentiment analysis and text network analysis of data from academic literature on digital software usage, types of qualitative data, qualitative analysis methods, analysis steps, and citations of notable publications in the field of big qualitative analysis methods. Results: 520 peer-reviewed studies and 37,129 internet posts were systematically reviewed. Web and social media posts referencing large qualitative data sets presented negative sentiments and many posts expressed ambiguity surrounding the categorization of digital and computational methods within the qualitative data analysis discipline. Over 50 types of digital software, and several collaborative qualitative data analysis methods and steps were identified. A LISTEN method manual has been developed to train and support the implementation of the method at three different sites, as well as the development of an interactive living systematic review. Conclusions: The newly developed LISTEN method will provide research teams with the flexibility to triangulate different types of data and combine the strengths of rapid research designs and digital methods

An enhanced deep learning‐based phishing detection mechanism to effectively identify malicious URLs using variational autoencoders

Abstract Phishing attacks have become one of the powerful sources for cyber criminals to impose various forms of security attacks in which fake website Uniform Resource Locators (URL) are circulated around the Internet community in the form of email, messages etc., in order to deceive users, resulting in the loss of their valuable assets. The phishing URLs are predicted using several blacklist‐based traditional phishing website detection techniques. However, numerous phishing websites are frequently constructed and launched on the Internet over time; these blacklist‐based traditional methods do not accurately predict most phishing websites. In order to effectively identify malicious URLs, an enhanced deep learning‐based phishing detection approach has been proposed by integrating the strength of Variational Autoencoders (VAE) and deep neural networks (DNN). In the proposed framework, the inherent features of a raw URL are automatically extracted by the VAE model by reconstructing the original input URL to enhance phishing URL detection. For experimentation, around 1 lakh URLs were crawled from two publicly available datasets, namely ISCX‐URL‐2016 dataset and Kaggle dataset. The experimental results suggested that the proposed model has reached a maximum accuracy of 97.45% and exhibits a quicker response time of 1.9 s, which is better when compared to all the other experimented models

LISTEN - Collaborative and Digital Analysis of Big Qualitative Data in Time Sensitive Contexts

Our project will develop, implement, evaluate and disseminate the Collaborative and Digital Analysis of Big Qual Data in Time Sensitive Contexts (LISTEN) method