Challenges and Pragmatic Solutions for Assessing the Reliability of HIV-1 Viral Load Monitoring in Resource-Constrained Settings.

INTRODUCTION: HIV-1 RNA detection is the most reliable method for monitoring treatment response among people living with HIV. Effective quality control measures that include internal quality control (IQC) are challenging in resource-constrained settings. METHODS: We ascertained the utility of the kit low positive control (LPC) as an effective IQC to monitor the reliability of the HIV-1 viral load assay. Variations in LPC values were measured for 390 different runs over 10 years (2011-2021) and compared to in-house IQC data using Levey-Jennings control chart. RESULTS: Overall, the Levey-Jennings analysis showed minimal variation (±0.5 log) for both the LPC and IQC data. The mean LPC value for first 20 runs (20 days) was 2.91. The mean LPC value for the 390 runs comprising 35 different lots was 3.01 ± 0.1 log. CONCLUSION: Our decadal data reveal that Abbott RealTime HIV-1 assay (Abbott Molecular Inc., IL, USA) LPC exhibited no significant biological variation over 390 runs distributed over 10 years. Hence, assay LPC can supplant the IQC for monitoring assay trends as a stable and commutable material in resource-constrained settings.

Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Plantations.

Mechanical weed management is a drudging task that requires manpower and has risks when conducted within rows of orchards. However, intrarow weeding must still be conducted by manual labor due to the restricted movements of riding mowers within the rows of orchards due to their confined structures with nets and poles. However, autonomous robotic weeders still face challenges identifying uncut weeds due to the obstruction of Global Navigation Satellite System (GNSS) signals caused by poles and tree canopies. A properly designed intelligent vision system would have the potential to achieve the desired outcome by utilizing an autonomous weeder to perform operations in uncut sections. Therefore, the objective of this study is to develop a vision module using a custom-trained dataset on YOLO instance segmentation algorithms to support autonomous robotic weeders in recognizing uncut weeds and obstacles (i.e., fruit tree trunks, fixed poles) within rows. The training dataset was acquired from a pear orchard located at the Tsukuba Plant Innovation Research Center (T-PIRC) at the University of Tsukuba, Japan. In total, 5000 images were preprocessed and labeled for training and testing using YOLO models. Four versions of edge-device-dedicated YOLO instance segmentation were utilized in this research-YOLOv5n-seg, YOLOv5s-seg, YOLOv8n-seg, and YOLOv8s-seg-for real-time application with an autonomous weeder. A comparison study was conducted to evaluate all YOLO models in terms of detection accuracy, model complexity, and inference speed. The smaller YOLOv5-based and YOLOv8-based models were found to be more efficient than the larger models, and YOLOv8n-seg was selected as the vision module for the autonomous weeder. In the evaluation process, YOLOv8n-seg had better segmentation accuracy than YOLOv5n-seg, while the latter had the fastest inference time. The performance of YOLOv8n-seg was also acceptable when it was deployed on a resource-constrained device that is appropriate for robotic weeders. The results indicated that the proposed deep learning-based detection accuracy and inference speed can be used for object recognition via edge devices for robotic operation during intrarow weeding operations in orchards.

Efficiency and Security Evaluation of Lightweight Cryptographic Algorithms for Resource-Constrained IoT Devices.

The IoT has become an integral part of the technological ecosystem that we all depend on. The increase in the number of IoT devices has also brought with it security concerns. Lightweight cryptography (LWC) has evolved to be a promising solution to improve the privacy and confidentiality aspect of IoT devices. The challenge is to choose the right algorithm from a plethora of choices. This work aims to compare three different LWC algorithms: AES-128, SPECK, and ASCON. The comparison is made by measuring various criteria such as execution time, memory utilization, latency, throughput, and security robustness of the algorithms in IoT boards with constrained computational capabilities and power. These metrics are crucial to determine the suitability and help in making informed decisions on choosing the right cryptographic algorithms to strike a balance between security and performance. Through the evaluation it is observed that SPECK exhibits better performance in resource-constrained IoT devices.

Multimodal Environmental Sensing Using AI & IoT Solutions: A Cognitive Sound Analysis Perspective.

This study presents a novel audio compression technique, tailored for environmental monitoring within multi-modal data processing pipelines. Considering the crucial role that audio data play in environmental evaluations, particularly in contexts with extreme resource limitations, our strategy substantially decreases bit rates to facilitate efficient data transfer and storage. This is accomplished without undermining the accuracy necessary for trustworthy air pollution analysis while simultaneously minimizing processing expenses. More specifically, our approach fuses a Deep-Learning-based model, optimized for edge devices, along with a conventional coding schema for audio compression. Once transmitted to the cloud, the compressed data undergo a decoding process, leveraging vast cloud computing resources for accurate reconstruction and classification. The experimental results indicate that our approach leads to a relatively minor decrease in accuracy, even at notably low bit rates, and demonstrates strong robustness in identifying data from labels not included in our training dataset.

Management of thromboembolic disorders during pregnancy in resource-constrained settings: An Indian perspective.

Venous thromboembolism (VTE) in pregnancy and resulting thrombotic disorders are increasingly being recognized as an important cause of maternal morbidity and mortality. The diagnosis of VTE during pregnancy has an impact on current as well as future foeto-maternal outcomes. Whereas algorithms to manage VTEs during pregnancy in developed countries exist, these are difficult to implement in resource-constraint settings. In this narrative review, we discuss strategies that can be applied in daily clinical practice by obstetricians and haematologists dealing with these disorders in the country.

Single center experience of therapeutic plasma exchange in a resource-constrained setting: A study of trends in scope and complications.

BACKGROUND: Therapeutic apheresis procedures are becoming an increasingly integral part of modern medical practice, be it as a part of therapy or pre-conditioning regimes for solid organ transplants. In our center, we follow the American Society for Apheresis (ASFA) guidelines for categorizing these procedures. However, lack of a centralized registry for therapeutic apheresis in India, lack of consolidated data as well as a resource-constrained setting prevent it from being utilized to its full potential. STUDY DESIGN AND METHODS: This study was a retrospective analysis of therapeutic plasma exchange (TPE) procedures performed from January 2015 to October 2022 in the Department of Transfusion Medicine at a large tertiary care hospital in North India. All consecutive TPE procedures were included. Overall and specialty-wise scoring for all patients was performed. Mean scores were calculated. RESULTS: A total of 1434 procedures were performed during the study duration of 7 years. These procedures were performed for 284 different patients. Majority of the procedures were referred from nephrology (895 of 1434, 62.4%), followed by neurology, gastroenterology, and liver transplant teams, hematology, critical care, rheumatology, pediatrics, and internal medicine. Complete response, partial response, and no-response were observed in 1077 (75.1%), 201 (14%), and 156 (10.9%) procedures respectively. Only 14 procedures reported adverse effects. DISCUSSION: Increasing effectiveness of TPE in a number of procedures and a variety of indications has broadened its scope, while the small number of adverse events, when supervised by trained Transfusion Medicine physicians has made TPE a more viable and safer alternative to other treatment modalities.

'Guidance should have been there 15 years ago' research stakeholders' perspectives on ancillary care in the global south: a case study of Malawi.

BACKGROUND: Medical researchers in resource-constrained settings must make difficult moral decisions about the provision of ancillary care to participants where additional healthcare needs fall outside the scope of the research and are not provided for by the local healthcare system. We examined research stakeholder perceptions and experiences of ancillary care in biomedical research projects in Malawi. METHODS: We conducted 45 qualitative in-depth interviews with key research stakeholders: researchers, health officials, research ethics committee members, research participants and grants officers from international research funding organisations. Thematic analysis was used to analyse and interpret the findings. FINDINGS: All stakeholders perceived the provision of ancillary care to have potential health benefits to study participants in biomedical research. However, they also had concerns, particularly related to the absence of guidance to support it. Some suggested that consideration for ancillary care provision could be possible on a case-by-case basis but that most of the support from research projects should be directed towards strengthening the public health system, emphasising public good above individual or personal benefits. Some researchers and ethics committee members raised concerns about potential tensions in terms of funding, for example balancing study demands with addressing participants' additional health needs. CONCLUSION: Our findings highlight the complexities and gaps in the guidance around the provision of ancillary care in Malawi and other resource-constrained settings more generally. To promote the provision of ancillary care, we recommend that national and international guidelines for research ethics include specific recommendations for resource-constrained settings and specific types of research.

Praxis-Informed Pointers: A Student Guide for Optimizing Clinical Learning in a Resource-Constrained Setting.

Issue: Health professions education (HPE) is intimately linked with teaching and learning in the clinical environment. While the value of authentic clinical experiences is acknowledged, whether learning actually occurs is to a large extent dependent on students' behaviors and attitudes. The kinds of student behaviors and attitudes that are necessary to optimize learning in the clinical environment thus becomes relevant. Evidence: Tips and recommendations to maximize clinical learning in a variety of settings have been well documented. There is, however, a dearth of literature which takes a narrative-based praxis approach focused on resource-constrained environments. We developed this praxis-orientated article as a means to translate the available literature and theory into a simple, practical guide, focused on optimizing clinical learning from a student perspective, remaining cognizant of the particular challenges present in a resource-constrained setting. Implications: Based on the resource-constrained environments our students are exposed to, we outline the following six key aspects: student-driven learning, integration into the community of practice, student engagement, empathy, interprofessional learning opportunities, and feedback for learning. These aspects provide useful pointers for students in general. Furthermore, exploration into what strategies students may utilize in resource-constrained clinical contexts is addressed.

"We just don't have the resources": Supervisor perspectives on introducing workplace-based assessments into medical specialist training in South Africa.

BACKGROUND: South Africa (SA) is on the brink of implementing workplace-based assessments (WBA) in all medical specialist training programmes in the country. Despite the fact that competency-based medical education (CBME) has been in place for about two decades, WBA offers new and interesting challenges. The literature indicates that WBA has resource, regulatory, educational and social complexities. Implementing WBA would therefore require a careful approach to this complex challenge. To date, insufficient exploration of WBA practices, experiences, perceptions, and aspirations in healthcare have been undertaken in South Africa or Africa. The aim of this study was to identify factors that could impact WBA implementation from the perspectives of medical specialist educators. The outcomes being reported are themes derived from reported potential barriers and enablers to WBA implementation in the SA context. METHODS: This paper reports on the qualitative data generated from a mixed methods study that employed a parallel convergent design, utilising a self-administered online questionnaire to collect data from participants. Data was analysed thematically and inductively. RESULTS: The themes that emerged were: Structural readiness for WBA; staff capacity to implement WBA; quality assurance; and the social dynamics of WBA. CONCLUSIONS: Participants demonstrated impressive levels of insight into their respective working environments, producing an extensive list of barriers and enablers. Despite significant structural and social barriers, this cohort perceives the impending implementation of WBA to be a positive development in registrar training in South Africa. We make recommendations for future research, and to the medical specialist educational leaders in SA.

Machine Learning Analytic-Based Two-Staged Data Management Framework for Internet of Things.

In applications of the Internet of Things (IoT), where many devices are connected for a specific purpose, data is continuously collected, communicated, processed, and stored between the nodes. However, all connected nodes have strict constraints, such as battery usage, communication throughput, processing power, processing business, and storage limitations. The high number of constraints and nodes makes the standard methods to regulate them useless. Hence, using machine learning approaches to manage them better is attractive. In this study, a new framework for data management of IoT applications is designed and implemented. The framework is called MLADCF (Machine Learning Analytics-based Data Classification Framework). It is a two-stage framework that combines a regression model and a Hybrid Resource Constrained KNN (HRCKNN). It learns from the analytics of real scenarios of the IoT application. The description of the Framework parameters, the training procedure, and the application in real scenarios are detailed. MLADCF has shown proven efficiency by testing on four different datasets compared to existing approaches. Moreover, it reduced the global energy consumption of the network, leading to an extended battery life of the connected nodes.

A Novel Approach of a Low-Cost Voltage Fault Injection Method for Resource-Constrained IoT Devices: Design and Analysis.

The rapid development of the Internet of Things (IoT) has brought about the processing and storage of sensitive information on resource-constrained devices, which are susceptible to various hardware attacks. Fault injection attacks (FIAs) stand out as one of the most widespread. Particularly, voltage-based FIAs (V-FIAs) have gained popularity due to their non-invasive nature and high effectiveness in inducing faults by pushing the IoT hardware to its operational limits. Improving the security of devices and gaining a comprehensive understanding of their vulnerabilities is of utmost importance. In this study, we present a novel fault injection method and employ it to target an 8-bit AVR microcontroller. We identify the optimal attack parameters by analyzing the detected failures and their trends. A case study is conducted to validate the efficacy of this new method in a more realistic scenario, focusing on a simple authentication method using the determined optimal parameters. This analysis not only demonstrates the feasibility of the V-FIA but also elucidates the primary characteristics of the resulting failures and their propagation in resource-constrained devices. Additionally, we devise a hardware/software countermeasure that can be integrated into any resource-constrained device to thwart such attacks in IoT scenarios.

An Impact Localization Solution Using Embedded Intelligence-Methodology and Experimental Verification via a Resource-Constrained IoT Device.

Recent advances both in hardware and software have facilitated the embedded intelligence (EI) research field, and enabled machine learning and decision-making integration in resource-scarce IoT devices and systems, realizing "conscious" and self-explanatory objects (smart objects). In the context of the broad use of WSNs in advanced IoT applications, this is the first work to provide an extreme-edge system, to address structural health monitoring (SHM) on polymethyl methacrylate (PPMA) thin-plate. To the best of our knowledge, state-of-the-art solutions primarily utilize impact positioning methods based on the time of arrival of the stress wave, while in the last decade machine learning data analysis has been performed, by more expensive and resource-abundant equipment than general/development purpose IoT devices, both for the collection and the inference stages of the monitoring system. In contrast to the existing systems, we propose a methodology and a system, implemented by a low-cost device, with the benefit of performing an online and on-device impact localization service from an agnostic perspective, regarding the material and the sensors' location (as none of those attributes are used). Thus, a design of experiments and the corresponding methodology to build an experimental time-series dataset for impact detection and localization is proposed, using ceramic piezoelectric transducers (PZTs). The system is excited with a steel ball, varying the height from which it is released. Based on TinyML technology for embedding intelligence in low-power devices, we implement and validate random forest and shallow neural network models to localize in real-time (less than 400 ms latency) any occurring impacts on the structure, achieving higher than 90% accuracy.

Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning.

The recent advancements in the Internet of Things have made it converge towards critical infrastructure automation, opening a new paradigm referred to as the Industrial Internet of Things (IIoT). In the IIoT, different connected devices can send huge amounts of data to other devices back and forth for a better decision-making process. In such use cases, the role of supervisory control and data acquisition (SCADA) has been studied by many researchers in recent years for robust supervisory control management. Nevertheless, for better sustainability of these applications, reliable data exchange is crucial in this domain. To ensure the privacy and integrity of the data shared between the connected devices, access control can be used as the front-line security mechanism for these systems. However, the role engineering and assignment propagation in access control is still a tedious process as its manually performed by network administrators. In this study, we explored the potential of supervised machine learning to automate role engineering for fine-grained access control in Industrial Internet of Things (IIoT) settings. We propose a mapping framework to employ a fine-tuned multilayer feedforward artificial neural network (ANN) and extreme learning machine (ELM) for role engineering in the SCADA-enabled IIoT environment to ensure privacy and user access rights to resources. For the application of machine learning, a thorough comparison between these two algorithms is also presented in terms of their effectiveness and performance. Extensive experiments demonstrated the significant performance of the proposed scheme, which is promising for future research to automate the role assignment in the IIoT domain.

Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing.

Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains, i.e., Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to further explore and advance the field of human action recognition in smart living.

What tuberculosis infection control measures are effective in resource-constrained primary healthcare facilities? A systematic review of the literature.

INTRODUCTION: Tuberculosis (TB) remains a major global health challenge, killing millions of people, despite the availability of preventive TB medication. The majority of these infections and deaths occur in low-income countries. Therefore, practical public health strategies are required to reduce the global TB burden in these countries effectively. The purpose of this review was to examine the current evidence of tuberculosis infection control (TBIC) measures in reducing TB transmission and explore the barriers and enablers of TBIC measures in resource-constrained primary healthcare settings. METHODS: The PRISMA framework was adopted to identify studies that report on the evidence and barriers and facilitators of administrative, environmental and respiratory control measures at healthcare settings in low- and middle-income countries (LMICs). ProQuest, Scopus, ScienceDirect, Embase and PubMed were searched for English language peer-reviewed studies published since the introduction of TBIC guidelines. Studies not relevant to the topic, were not on TBIC measures or were reviews or commentary-style papers were excluded. Included articles were evaluated based on their aim, study design, geography and health settings interventions (TBIC measures), economic setting (ie LMICs) and main findings. RESULTS: Our review of the 15 included studies identified a cough officer screening system, isolation of TB patients, modification of consultation rooms, and opening windows and doors as effective TB prevention measures. Lack of patient education, unsupportive workplace culture, inadequate supply of particulate respirators, insufficient isolation facilities and poor physical infrastructures were identified as barriers to TBIC practices. Triaging TB patients, maintenance of health infrastructure, appropriate use of personal protective equipment (PPE) and healthcare workers (HCWs) training on the correct use of PPE were reported as facilitators of TBIC in primary healthcare facilities. CONCLUSION: Our review provides consistent evidence of TBIC measures in reducing TB transmission in resource-constrained primary healthcare settings. This review has demonstrated that TB transmission can be successfully controlled using multiple and simple low-cost TBIC measures including administrative, environmental and respiratory controls. Effective implementation of triaging patients with suspected TB alongside maintenance of health infrastructure, appropriate use of PPE and robust HCWs training on TBIC could improve implementation of TBIC measures in primary healthcare settings. Healthcare management should address these areas particularly in rural and remote locations to improve the implementation of TBIC measures in primary healthcare facilities in LMICs.

Improving Underweight Mothers' Essential Newborn Care During Early Infancy: A Single-Blinded, Parallel-Randomized, Controlled Trial.

OBJECTIVE: To evaluate the effectiveness of using a standardized Essential Newborn Care (ENC) module taught by pediatric residents on ENC skills and growth of offspring born to underweight primigravida mothers. STUDY DESIGN: This facility-based, single-blinded, parallel, randomized controlled trial was conducted between May 2018 and March 2019. Eighty-eight underweight primigravida mothers and their vaginally delivered offspring were blindly allocated into the intervention group (IG) or control group (CG). The IG mothers received education on ENC through pictorial aids, demonstrations, and practice sessions. All mothers received information from ongoing public health programs. A trained hospital nurse, blinded to the study, assessed the mothers' neonatal care skills on the second postnatal day. The infants were followed until 6 months. Weight, length, and head circumference were measured at birth and age 6 weeks, 10 weeks, 14 weeks, and 6 months (±1 week). RESULTS: Mothers in the IG had significantly better ENC skills in all domains (P < .001). Their infants had a statistically significant increase in weight (at 10 and 14 weeks and 6 months), length (at 14 weeks and 6 months), and head circumference (at 6 months). Infants' z-scores indicated significant improvements in anthropometry in the IG compared with the CG. At age 6 months, the number of infants with weight <3rd percentile decreased in the IG (from 20 of 44 to 5 of 41) and increased in the CG (from 17 of 44 to 22 of 42) compared with birth percentiles. CONCLUSIONS: An educational intervention to strengthen maternal ENC knowledge and skills soon after delivery improved physical growth in infants born to underweight primigravida mothers. TRIAL REGISTRATION: Clinical Trials Registry-India: CTRI/2018/04/013096.

Biosensors for the detection of disease outbreaks through wastewater-based epidemiology.

Wastewater-Based Epidemiology (WBE) is a novel community-wide monitoring tool that provides comprehensive real-time data of the public and environmental health status and can contribute to public health interventions, including those related to infectious disease outbreaks (e.g., the ongoing COVID-19 pandemic). Nonetheless, municipalities without centralized laboratories are likely still not able to process WBE samples. Biosensors are a potentially cost-effective solution to monitor the development of diseases through WBE to prevent local outbreaks. This review discusses the economic and technical feasibility of eighteen recently developed biosensors for the detection and monitoring of infectious disease agents in wastewater, prospecting the prevention of future pandemics.

Shifting gears to differentiation agents in acute promyelocytic leukemia with resource constraints-a cohort study.

BACKGROUND: Treatment of acute promyelocytic leukaemia has emerged as a major success in hemato-oncology. While literature from the developed world boasts of outstanding outcomes, there is a paucity of data from the developing world. This study aimed to assess complications and outcomes of acute promyelocytic leukaemia in a resource-constrained setting. METHODS: We retrospectively collected data from patients diagnosed with APL from January 2016 to December 2020. RESULTS: Sixty-four patients were treated-32 in both the Sanz high and low-risk groups. In the Sanz low-risk group, 12.5% of patients received ATRA with daunorubicin and 81.25% received ATRA with ATO. In the Sanz high-risk group, 18.8% of patients received ATRA with daunorubicin, 34.3% received ATRA with daunorubicin and ATO while 40.6% received ATRA with ATO. 56.25% of patients developed differentiation syndrome. The incidence was higher in Sanz high-risk group as compared to Sanz low-risk group. 57.4% of patients had an infection at the time of presentation. 62.5% of patients developed neutropenic fever during treatment. 17.2% of patients developed pseudotumor cerebri. The 4-year EFS and OS were 71.25 and 73.13%, respectively. Sanz low-risk group had a better 4-year EFS and OS as compared to the Sanz high-risk group. Haemoglobin at presentation and Sanz high-risk group were associated with poorer outcomes with a hazard ratio of 0.8 and 3.1, respectively. Outcomes in high-risk patients were better with the use of ATRA + ATO + daunorubicin. CONCLUSION: In the Indian population, APL patients have a high incidence of differentiation syndrome, pseudotumor cerebri, and infections during induction. CR, EFS, and OS compared to the developed world can be achieved with optimal therapy. Low haemoglobin at presentation and Sanz high-risk group were associated with poorer outcomes. ATRA, ATO, and daunorubicin combination is the preferred protocol for treating high-risk patients.

Passive In-Line Chlorination for Drinking Water Disinfection: A Critical Review.

The world is not on track to meet Sustainable Development Goal 6.1 to provide universal access to safely managed drinking water by 2030. Removal of priority microbial contaminants by disinfection is one aspect of ensuring water is safely managed. Passive chlorination (also called in-line chlorination) represents one approach to disinfecting drinking water before or at the point of collection (POC), without requiring daily user input or electricity. In contrast to manual household chlorination methods typically implemented at the point of use (POU), passive chlorinators can reduce the user burden for chlorine dosing and enable treatment at scales ranging from communities to small municipalities. In this review, we synthesized evidence from 27 evaluations of passive chlorinators (in 19 articles, 3 NGO reports, and 5 theses) conducted across 16 countries in communities, schools, health care facilities, and refugee camps. Of the 27 passive chlorinators we identified, the majority (22/27) were solid tablet or granular chlorine dosers, and the remaining devices were liquid chlorine dosers. We identified the following research priorities to address existing barriers to scaled deployment of passive chlorinators: (i) strengthening local chlorine supply chains through decentralized liquid chlorine production, (ii) validating context-specific business models and financial sustainability, (iii) leveraging remote monitoring and sensing tools to monitor real-time chlorine levels and potential system failures, and (iv) designing handpump-compatible passive chlorinators to serve the many communities reliant on handpumps as a primary drinking water source. We also propose a set of reporting indicators for future studies to facilitate standardized evaluations of the technical performance and financial sustainability of passive chlorinators. In addition, we discuss the limitations of chlorine-based disinfection and recognize the importance of addressing chemical contamination in drinking water supplies. Passive chlorinators deployed and managed at-scale have the potential to elevate the quality of existing accessible and available water services to meet "safely managed" requirements.

A chronological discourse analysis of ancillary care provision in guidance documents for research conduct in the global south.

INTRODUCTION: Numerous guidelines and policies for ethical research practice have evolved over time, how this translates to global health practice in resource-constrained settings is unclear. The purpose of this paper is to describe how the concept of ancillary care has evolved over time and how it is included in the ethics guidelines and policy documents that guide the conduct of research in the global south with both an international focus and providing a specific example of Malawi, where the first author lives and works, as a case study. METHODS: Discourse analysis was conducted on 34 international ethics guidelines and policy documents. Documents were purposively selected if they contained a set of key terms that reflect the concept of ancillary care. Following a process of inductive discourse analysis, five key interrelated text phrases relating to ancillary care were extracted from the documents. The evolution of these phrases over time was explored as they represented the development of the concept of ancillary care as a component of ethical health research guidance and practice. RESULTS: We found key interrelated phrases that represent discourses regarding the evolution of ancillary care including participant protection; provide care as appropriate; supererogation; patient needs prevail over science; and ancillary care as an obligation. Arguments for the provision of ancillary care were characterised by safeguarding the safety, health rights and well-being of study participants. However, despite the evolution of discourse around ethical obligations to provide ancillary care, this is rarely made explicit within guidance documents, leaving interpretive space for differential application in practice. CONCLUSION: While there have been major changes to the ethics guidance that reflect significant evolution in the ethical conduct of research, the specific vocabulary or language used to explain the ethics of researchers' ancillary care obligations to the health needs of their research participants, lacks clarity and consistency. As a result, the concept of ancillary care continues to be under-represented in local ethical guidelines and regulations, with no clear directives for country-level research ethics committees to apply in regulating ancillary care responsibilities.