Research Track Research Articles

Keeping Black students in STEM

For years, STEM programs have tried to attract Black grads and undergrads. There’s a new push to retain these students—and emulate the university program that seems to have had the most success. The science curriculum at Akeema Onalaja’s high school in South Los Angeles, CA, ended with biology. Yet she pursued a career in biochemistry, knowing that becoming a researcher—let alone one who is Black—wouldn’t be easy. Initially, she did well at Mount Saint Mary’s University, Los Angeles in CA. But after an unexpected death in the family, her grades plummeted sophomore year. “I came from a single-parent household where drug abuse made it so I couldn’t even go home on the weekends…there aren’t a lot of professors who can relate,” says Onalaja, recalling only one of her all-White and seemingly middle-class professors asking about the drop in her grades. Persevering, Onalaja graduated with a 3.0 GPA. She still aspired to attend graduate school, although it would turn out to be a tall order. Despite attempts to level the playing field, Black college students continue to leave STEM at higher rates than their White peers. Several university programs are trying to change that trend. Image credit: Dave Cutler (artist). “I knew some of the conversations that my teachers had with other students applying to grad school, and I felt like I didn't get the same level of care,” Onalaja says. “That made me believe I really couldn't make it, and getting my first rejection letter from grad school solidified what I had thought.” After being denied admission to a graduate program, Onalaja left the science, technology, engineering, and mathematics (STEM) research track in 2014 for a science-related sales position. She takes responsibility for this turn. Had Onalaja been more open about her struggles, she says she perhaps could have received …

Stay on Top of the Latest Breakthroughs with the ANESTHESIOLOGY® 2021 Research Track

Article| June 2021 Stay on Top of the Latest Breakthroughs with the ANESTHESIOLOGY® 2021 Research Track Jeffrey R. Kirsch, MD, FASA Jeffrey R. Kirsch, MD, FASA Search for other works by this author on: This Site PubMed Google Scholar ASA Monitor June 2021, Vol. 85, 24–25. https://doi.org/10.1097/01.ASM.0000754236.65424.a9 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Cite Icon Cite Get Permissions Search Site Citation Jeffrey R. Kirsch; Stay on Top of the Latest Breakthroughs with the ANESTHESIOLOGY® 2021 Research Track. ASA Monitor 2021; 85:24–25 doi: https://doi.org/10.1097/01.ASM.0000754236.65424.a9 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentAll PublicationsASA Monitor Search Advanced Search Topics: covid-19, information management, mentors, neuroscience, neurotoxicity syndromes, pain, peer group, perioperative medicine, physician executives, posters Meet us in San Diego this October to explore the latest science in the specialty, delivered by leading researchers and subject matter experts, and connect with the greatest minds in anesthesiology. Emerging research will continue to receive greater prominence as we drive to always deliver top-tier, relevant education. This year's research track includes more than 20 research-focused sessions and events, including Anesthesiology journal activities and FAER sessions, as well as paper and electronic abstract presentations. Plus, the research lounge is back by popular demand. It's the ideal spot to network with peers or meet with a mentor while on site in San Diego. The ANESTHESIOLOGY 2021 research track includes: John W. Severinghaus Lecture on Translational Science: Gadgeteering for Pain Relief James C. Eisenach, MD FAER-Helrich Research Lecture: Crisis and Opportunity in Clinical Trials Daniel I. Sessler, MD Celebration of Research Evan D. Kharasch, MD, PHD Journal Symposium: SARS-CoV-2 and COVID19:... You do not currently have access to this content.

Preface

The fifth International Conference on Data Mining, Communications and Information Technology (DMCIT 2021) was held in Hangzhou, China from April 16 to 18, 2021, which is organized by Asia Pacific Institute of Science and Engineering (APISE), and media supported by MDPI-Mathematics. The conference strives to offer plenty of networking opportunities to meet and interact with the world-leading scientists, engineers and researchers as well as industrial partners in Data Mining, Communication Engineering and Information Technology Applications.DMCIT has been held for four years since 2017 after four successful conferences in Phuket, Shanghai, Beijing, and Virtually (2020), attracting a large number of researchers, scholars and students from almost all over the world. DMCIT 2021 is the fifth event and we continue to provide a unique and excellent forum to exchange research methodologies, explore practical applications, and foster innovative ideas, in Data Mining, Communication Engineering and Information Technology Applications. The conference was originally scheduled in Hangzhou from April 16 to 18, 2021. Due to the outbreak of COVID-19, we follow most current conferences and organize it as a hybrid conference, participants who cannot attend offline can also join online.The proceedings of this year’s edition consists of 5 main categories: 1, Intelligent Processing based on Reasoning and Models; 2, Machine Learning Research and Appllications; 3, Classification Methods and Applications; 4, Mathamatical Modeling and Optimization; 5, Simulation and Implementation of Data Centric Approaches. The research tracks attracted 105 submissions from about 11 countries, among which 46 were accepted. All the submissions were rigorously reviewed by the conference Program Committee (PC). We have also selected 2 best oral presenter award and 1 best poster award. Four keynote speakers were invited to share their latest and insightful research ideas in the conference.We would like to take this opportunity to thank all the authors for submitting their papers to our conference. We also extend our appreciation to the invited speakers, PC members, supporting staff, as well as all the Organizing Committee members for their hard work, which eventually makes DMCIT 2021 a successful and fruitful event! Yucong Duan, Hainan University, China Conference Committee Chair, DMCIT 2021

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

Contrastive self-supervised clustering of scRNA-seq data

BackgroundSingle-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq data and its significant sparsity accentuated by frequent dropout events, introducing false zero count observations, make the clustering analysis computationally challenging. Even though multiple scRNA-seq clustering techniques have been proposed, there is no consensus on the best performing approach. On a parallel research track, self-supervised contrastive learning recently achieved state-of-the-art results on images clustering and, subsequently, image classification.ResultsWe propose contrastive-sc, a new unsupervised learning method for scRNA-seq data that perform cell clustering. The method consists of two consecutive phases: first, an artificial neural network learns an embedding for each cell through a representation training phase. The embedding is then clustered in the second phase with a general clustering algorithm (i.e. KMeans or Leiden community detection). The proposed representation training phase is a new adaptation of the self-supervised contrastive learning framework, initially proposed for image processing, to scRNA-seq data. contrastive-sc has been compared with ten state-of-the-art techniques. A broad experimental study has been conducted on both simulated and real-world datasets, assessing multiple external and internal clustering performance metrics (i.e. ARI, NMI, Silhouette, Calinski scores). Our experimental analysis shows that constastive-sc compares favorably with state-of-the-art methods on both simulated and real-world datasets.ConclusionOn average, our method identifies well-defined clusters in close agreement with ground truth annotations. Our method is computationally efficient, being fast to train and having a limited memory footprint. contrastive-sc maintains good performance when only a fraction of input cells is provided and is robust to changes in hyperparameters or network architecture. The decoupling between the creation of the embedding and the clustering phase allows the flexibility to choose a suitable clustering algorithm (i.e. KMeans when the number of expected clusters is known, Leiden otherwise) or to integrate the embedding with other existing techniques.

Soccer goalkeeper expertise identification based on eye movements.

By focusing on high experimental control and realistic presentation, the latest research in expertise assessment of soccer players demonstrates the importance of perceptual skills, especially in decision making. Our work captured omnidirectional in-field scenes displayed through virtual reality glasses to 12 expert players (picked by DFB), 10 regional league intermediate players, and13 novice soccer goalkeepers in order to assess the perceptual skills of athletes in an optimized manner. All scenes were shown from the perspective of the same natural goalkeeper and ended after the return pass to that goalkeeper. Based on the gaze behavior of each player, we classified their expertise with common machine learning techniques. Our results show that eye movements contain highly informative features and thus enable a classification of goalkeepers between three stages of expertise, namely elite youth player, regional league player, and novice, at a high accuracy of 78.2%. This research underscores the importance of eye tracking and machine learning in perceptual expertise research and paves the way for perceptual-cognitive diagnosis as well as future training systems.

Creating a Resident Research Track in Synergy With the Rehabilitation Medicine Scientist Training Program.

Physician scientists play an important role in the translation of research findings to patient care; however, their training faces numerous challenges. Residency research track programs represent an opportunity to facilitate the training of future physician scientists in physical medicine and rehabilitation, although optimal program organization and long-term outcomes remain unknown. The Rehabilitation Medicine Scientist Training Program is a National Institutes of Health-funded program aimed at addressing the shortage of physician researchers in the field of physical medicine and rehabilitation by providing instruction, mentorship, and networking opportunities for a successful research career. While the opportunities provided through the Rehabilitation Medicine Scientist Training Program provide critical education and guidance at a national level, trainees are most successful with availability of strong local support and mentorship. The purpose of this article was to present a realistic and easily applicable structure for a physical medicine and rehabilitation residency research track that can be used in concert with the Rehabilitation Medicine Scientist Training Program.

Investor relations in the era of social media: systematic literature review of social media as a strategic corporate disclosure tool

PurposeThis paper aims to systematically review how corporations are increasingly using social media to strategically disseminate information to investors, including different research tracks, then identify the gaps to propose future research opportunities.Design/methodology/approachThe authors searched for relevant scholarly work on Scopus and Google Scholar databases published during the period 2000–2020 in English. Both quantitative and qualitative papers were reviewed. Articles were filtered based on their relevance to the study's goal, resulting in the selection of 84 articles. A total of 16 articles were selected for inclusion in the systematic review.FindingsIn light of the existing studies’ limitations, this paper derives and summarizes 16 leading future research tracks. Results indicated that corporations could use social media to reduce information asymmetry between managers and investors. Nevertheless, social media for information disclosure purposes is used in a strategic way, whereby only positive news and voluntary information are disseminated.Research limitations/implicationsThe implications for investors are that they can make better decisions by engaging in the process of “the wisdom of crowd,” which is facilitated by reciprocal communication. The implications for corporations are that sharing earning information through social networking platforms presents them with an opportunity to effectively manage their investors by reducing negative perceptions and increasing market response.Originality/valueAs far as we know, this is the first paper that uses a systematic literature review over the social media research field.

Logistics and Supply Chain Management Research in Morocco: A Systematic Literature Review

This article has two objectives, the first is to present an analysis of the results of a systematic review of literature on logistics and supply chain management (SCM) in Morocco from 2010 to 2018, and second, to present a diagnosis of this research in order to propose a line of research which is little or not explored yet in the Moroccan context. To reach these objectives, we rely on the systematic review approach of literature and 51 articles were selected for final analysis. The results show that some laboratories are more active in logistics and SCM in the Moroccan context than others. Also, most of the articles included present a theoretical background that is not totally explored. Furthermore, our results reveal a dominance of articles highlighting logistics performance. This study presents a number of research tracks which take into consideration the recent evolutions in the logistics sector in Morocco.

Log-based software monitoring: a systematic mapping study

Modern software development and operations rely on monitoring to understand how systems behave in production. The data provided by application logs and runtime environment are essential to detect and diagnose undesired behavior and improve system reliability. However, despite the rich ecosystem around industry-ready log solutions, monitoring complex systems and getting insights from log data remains a challenge. Researchers and practitioners have been actively working to address several challenges related to logs, e.g., how to effectively provide better tooling support for logging decisions to developers, how to effectively process and store log data, and how to extract insights from log data. A holistic view of the research effort on logging practices and automated log analysis is key to provide directions and disseminate the state-of-the-art for technology transfer. In this paper, we study 108 papers (72 research track papers, 24 journals, and 12 industry track papers) from different communities (e.g., machine learning, software engineering, and systems) and structure the research field in light of the life-cycle of log data. Our analysis shows that (1) logging is challenging not only in open-source projects but also in industry, (2) machine learning is a promising approach to enable a contextual analysis of source code for log recommendation but further investigation is required to assess the usability of those tools in practice, (3) few studies approached efficient persistence of log data, and (4) there are open opportunities to analyze application logs and to evaluate state-of-the-art log analysis techniques in a DevOps context.

Self-calibration and Collaborative Localization for UWB Positioning Systems

Ultra-Wideband (UWB) is a Radio Frequency technology that is currently used for accurate indoor localization. However, the cost of deploying such a system is large, mainly due to the need for manually measuring the exact location of the installed infrastructure devices (“anchor nodes”). Self-calibration of UWB reduces deployment costs, because it allows for automatic updating of the coordinates of fixed nodes when they are installed or moved. Additionally, installation costs can also be reduced by using collaborative localization approaches where mobile nodes act as anchors. This article surveys the most significant research that has been done on self-calibration and collaborative localization. First, we find that often these terms are improperly used, leading to confusion for the readers. Furthermore, we find that in most of the cases, UWB-specific characteristics are not exploited, so crucial opportunities to improve performance are lost. Our classification and analysis provide the basis for further research on self-calibration and collaborative localization in the deployment of UWB indoor localization systems. Finally, we identify several research tracks that are open for investigation and can lead to better performance, e.g., machine learning and optimized physical settings.

الاجتماع فی النحو العربی بین ضوابط التقعید والواقع الاستعمالی

تتبع البحث ظاهرة الاجتماع بين نظيرين أو متقابلين التي عني بها النحويون في أبواب متفرقة، ووضعوا لها ضوابط قياسية، قد تتسع أحيانا؛ لتشمل الواقع الاستعمالي، فدرست هذه الظاهرة - من خلال المنهج الوصفي - في مجموعة من القضايا، مقارنا بين ضوابطها، والواقع الاستعمالي لها، ومحددا صورها من خلال الشواهد النحوية، وقد أثبت البحث أن قضايا الاجتماع في مجملها تدور بين التقديم والتأخير، والاتصال والانفصال، والاستئثار بالموقع، ومفارقة النوع إلى نوع آخر، کما لعبت الدلالة السياقية دورا محوريا في تحديد صور الاجتماع، کما رجح البحث بين آراء النحويين حول قضايا الاجتماع بناء على الواقع الاستعمالي، والدلالة. Co-occurrence in Arabic Grammar between Rule Setting Standards and Actual Use Waleed Moqbel ALSAYED ALY Al Deeb Department of Arabic Language, Jazan University E-mail: waldib@jazanu.edu.sa Abstract: The research tracks the phenomenon of the co-occurrence of two peers or opposing ones, which the grammarians are concerned with in separate sections, and they have set standard controls for it, which may sometimes be expanded to include the actual use. Thus the research has studied this phenomenon through the descriptive approach, in an array of issues, comparing their standards, and their actual use, and determining their phases through grammatical examples. The research also proved that most co-occurrence cases revolve around anastrophe, connection and separation , position monopolization and changing category. In addition, contextual semantics played a pivotal role in deciding on the co-occurrence type. Researchers have also balanced between grammarians’ views on co-occurrence cases according to the actual use and semantics.

Hybrid auditory fMRI: In pursuit of increasing data acquisition while decreasing the impact of scanner noise

BackgroundTwo challenges in auditory fMRI include the loud scanner noise during sound presentation and slow data acquisition. Here, we introduce a new auditory imaging protocol, termed “hybrid”, that alleviates these obstacles. New methodWe designed a within-subject experiment (N = 14) wherein language-driven activity was measured by hybrid, interleaved silent (ISSS), and continuous multiband acquisition. To determine the advantage of noise attenuation during sound presentation, hybrid was compared to multiband. To identify the benefits of increased temporal resolution, hybrid was compared to ISSS. Data were evaluated by whole-brain univariate general linear modeling (GLM) and multivariate pattern analysis (MVPA). ResultsComparison with existing methods:•Hybrid vs. Multiband: in both GLM and MVPA, hybrid showed widespread activation throughout the language network including the left inferior frontal gyrus, bilateral superior temporal regions, thalamus, and inferior colliculus. By contrast, multiband showed activity mostly within the left frontotemporal cortices.•Hybrid vs. ISSS: in MVPA, hybrid yielded more activation than ISSS throughout the language network. However, in GLM, hybrid detected less activation than ISSS. Despite the reduction of activation, hybrid more specifically detected activity in the canonical language network compared to ISSS. ConclusionsOur data revealed that hybrid imaging restored neural activity in the canonical language network that was absent due to the loud noise or slow sampling in the conventional imaging protocols. With its noise-attenuated sound presentation windows and increased acquisition speed, the hybrid protocol is well-suited for auditory fMRI research tracking neural activity pertaining to fast, time-varying acoustic events.

Preface

The 5th International Conference on Machine Vision and Information Technology (CMVIT 2021) was held on Feb. 26, 2021. It is organized by Federation University Australia, technically sponsored by Shanghai Jiaotong University and Xi’an Jiaotong-Liverpool University. The conference strives to offer plenty of networking opportunities to meet and interact with the world-leading scientists, engineers and researchers as well as industrial partners in machine vision and information technologies.CMVIT has been held for four years since 2017 after four successful conferences in Singapore, Hong Kong, Guangzhou, and Sanya, attracting a large number of researchers, scholars and students from all over the world. CMVIT 2021 is the fifth event and we continue to provide a unique and excellent forum to exchange research methodologies, explore practical applications, and foster innovative ideas, in machine vision and information technology. The conference was originally scheduled at Auckland, New Zealand from Feb 25 to 28, 2021. Due to the outbreak of COVID-19, we follow most current conferences and organize it as a fully virtual conference.The proceedings of this year’s edition consists of 3 main categories: Image and Signal Processing, Information Technology, and Machine Vision. The research tracks attracted 84 submissions from about 10 countries, among which 39 were accepted. All the submissions were rigorously reviewed by the conference Program Committee (PC). We have also selected 1 best paper award, 1 best poster award, and 2 runner-up paper awards. One plenary speaker and 2 keynote speakers were invited to share their latest and insightful research ideas in the conference.We would like to take this opportunity to thank all the authors for submitting their papers to our conference. We also extend our appreciation to the invited speakers, PC members, supporting staff, as well as all the Organizing Committee members for their hard work, which eventually makes CMVIT 2021 a successful and fruitful event!Kaizhu Huang, Xi’an Jiaotong-Liverpool University, ChinaPaul Pang, Federation University Australia, AustraliaQingbo He, Shanghai Jiao Tong University, ChinaGeneral Co-Chairs, CMVIT 2021

Teoria do Ensino Desenvolvimental e a organização do ensino dos conceitos de área e de perímetro nos anos iniciais do ensino fundamental

No presente artigo é apresentada a trajetória de investigação de doutorado que teve como objeto a organização do ensino da Matemática. A questão eixo de nossa investigação traduziu-se em: quais as contribuições da Teoria do Ensino Desenvolvimental para a organização do ensino dos conceitos de área e de perímetro nos anos iniciais do Ensino Fundamental? As discussões fundamentam-se na Teoria do Ensino Desenvolvimental, que embasa o desenvolvimento de um experimento didático, com a proposição de uma tarefa de estudo, e a análise dos seus resultados. A análise do experimento delineia o percurso formativo das capacidades de análise, planejamento e reflexão, componentes do pensamento teórico. Constatou-se, na análise do experimento, que o pensamento em nível teórico pode ser engendrado por meio da tarefa de estudo, quando as ações contemplem os movimentos do pensamento de redução do concreto ao abstrato e de ascensão do abstrato ao concreto.

Educational and scholarly opportunities for medical students and residents in the United States dermatology societies

Educational and scholarly opportunities for medical students and residents in the United States dermatology societies

Dosimetric evaluation of MRI-guided multi-leaf collimator tracking and trailing for lung stereotactic body radiation therapy.

PurposeThe treatment margins for lung stereotactic body radiotherapy (SBRT) are often large to cover the tumor excursions resulting from respiration, such that underdosage of the tumor can be avoided. Magnetic resonance imaging (MRI)‐guided multi‐leaf collimator (MLC) tracking can potentially reduce the influence of respiration to allow for smaller treatment margins. However, tracking is accompanied by system latency that may induce residual tracking errors. Alternatively, a simpler mid‐position delivery combined with trailing can be used. Trailing reduces influences of respiration by compensating for baseline motion, to potentially improve target coverage. In this study, we aim to show the feasibility of MRI‐guided tracking and trailing to reduce influences of respiration during lung SBRT.MethodsWe implemented MRI‐guided tracking on the MR‐linac using an Elekta research tracking interface to track tumor motion during intensity modulated radiotherapy (IMRT). A Quasar MRI4D phantom was used to generate Lujan motion (cos4, 4 s period, 20 mm peak‐to‐peak amplitude) with and without 1.0 mm/min cranial drift. Phantom tumor positions were estimated from sagittal 2D cine‐MRI (4 or 8 Hz) using cross‐correlation‐based template matching. To compensate the anticipated system latency, a linear ridge regression predictor was optimized for online MRI by comparing two predictor training approaches: training on multiple traces and training on a single trace. We created 15‐beam clinical‐grade lung SBRT plans for central targets (8 × 7.5 Gy) and peripheral targets (3 × 18 Gy) with different PTV margins for mid‐position based motion management (3–5 mm) and for MLC tracking (3 mm). We used a film insert with a 3 cm spherical target to measure the spatial distribution and quantity of the delivered dose. A 1%/1 mm local gamma‐analysis quantified dose differences between motion management strategies and reference cases. Additionally, a dose area histogram (DAH) revealed the target coverage relative to the reference scenario.ResultsThe prediction filter gain was on average 25% when trained on multiple traces and 44% when trained on a single trace. The filter reduced system latency from 313 ± 2 ms to 0 ± 5 ms for 4 Hz imaging and from 215 ± 3 ms to 3 ± 3 ms for 8 Hz. The local gamma analysis for the central delivery showed that tracking improved the gamma pass‐rate from 23% to 96% for periodic motion and from 14% to 93% when baseline drift was applied. For the peripheral delivery during periodic motion, delivery pass‐rates improved from 22% to 93%. Comparing mid‐position delivery to trailing for periodic+drift motion increased the local gamma pass rate from 15% to 98% for a central delivery and from 8% to 98% for a peripheral delivery. Furthermore, the DAHs revealed a relative D98% GTV coverage of 101% and 97% compared to the reference scenario for, respectively, central and peripheral tracking of periodic+drift motion. For trailing, a relative D98% of 99% for central and 98% for peripheral trailing was found.ConclusionsWe provided a first experimental demonstration of the technical feasibility of MRI‐guided MLC tracking and trailing for central and peripheral lung SBRT. Tracking maximizes the sparing of healthy tissue, while trailing is highly effective in mitigating baseline motion.

Kangaroo mother care: EN-BIRTH multi-country validation study

BackgroundKangaroo mother care (KMC) reduces mortality among stable neonates ≤2000 g. Lack of data tracking coverage and quality of KMC in both surveys and routine information systems impedes scale-up. This paper evaluates KMC measurement as part of the Every Newborn Birth Indicators Research Tracking in Hospitals (EN-BIRTH) study.MethodsThe EN-BIRTH observational mixed-methods study was conducted in five hospitals in Bangladesh, Nepal and Tanzania from 2017 to 2018. Clinical observers collected time-stamped data as gold standard for mother-baby pairs in KMC wards/corners. To assess accuracy, we compared routine register-recorded and women’s exit survey-reported coverage to observed data, using different recommended denominator options (≤2000 g and ≤ 2499 g). We analysed gaps in quality of provision and experience of KMC. In the Tanzanian hospitals, we assessed daily skin-to-skin duration/dose and feeding frequency. Qualitative data were collected from health workers and data collectors regarding barriers and enablers to routine register design, filling and use.ResultsAmong 840 mother-baby pairs, compared to observed 100% coverage, both exit-survey reported (99.9%) and register-recorded coverage (92.9%) were highly valid measures with high sensitivity. KMC specific registers outperformed general registers. Enablers to register recording included perceptions of data usefulness, while barriers included duplication of data elements and overburdened health workers. Gaps in KMC quality were identified for position components including wearing a hat. In Temeke Tanzania, 10.6% of babies received daily KMC skin-to-skin duration/dose of ≥20 h and a further 75.3% received 12–19 h. Regular feeding ≥8 times/day was observed for 36.5% babies in Temeke Tanzania and 14.6% in Muhimbili Tanzania. Cup-feeding was the predominant assisted feeding method. Family support during admission was variable, grandmothers co-provided KMC more often in Bangladesh. No facility arrangements for other family members were reported by 45% of women at exit survey.ConclusionsRoutine hospital KMC register data have potential to track coverage from hospital KMC wards/corners. Women accurately reported KMC at exit survey and evaluation for population-based surveys could be considered. Measurement of content, quality and experience of KMC need consensus on definitions. Prioritising further KMC measurement research is important so that high quality data can be used to accelerate scale-up of high impact care for the most vulnerable.

Respectful maternal and newborn care: measurement in one EN-BIRTH study hospital in Nepal

BackgroundRespectful maternal and newborn care (RMNC) is an important component of high-quality care but progress is impeded by critical measurement gaps for women and newborns. The Every Newborn Birth Indicators Research Tracking in Hospitals (EN-BIRTH) study was an observational study with mixed methods assessing measurement validity for coverage and quality of maternal and newborn indicators. This paper reports results regarding the measurement of respectful care for women and newborns.MethodsAt one EN-BIRTH study site in Pokhara, Nepal, we included additional questions during exit-survey interviews with women about their experiences (July 2017–July 2018). The questionnaire was based on seven mistreatment typologies: Physical; Sexual; or Verbal abuse; Stigma/discrimination; Failure to meet professional standards of care; Poor rapport between women and providers; and Health care denied due to inability to pay. We calculated associations between these typologies and potential determinants of health – ethnicity, age, sex, mode of birth – as possible predictors for reporting poor care.ResultsAmong 4296 women interviewed, none reported physical, sexual, or verbal abuse. 15.7% of women were dissatisfied with privacy, and 13.0% of women reported their birth experience did not meet their religious and cultural needs. In descriptive analysis, adjusted odds ratios and multivariate analysis showed primiparous women were less likely to report respectful care (β = 0.23, p-value < 0.0001). Women from Madeshi (a disadvantaged ethnic group) were more likely to report poor care (β = − 0.34; p-value 0.037) than women identifying as Chettri/Brahmin. Women who had caesarean section were less likely to report poor care during childbirth (β = − 0.42; p-value < 0.0001) than women with a vaginal birth. However, babies born by caesarean had a 98% decrease in the odds (aOR = 0.02, 95% CI, 0.01–0.05) of receiving skin-to-skin contact than those with vaginal births.ConclusionsMeasurement of respectful care at exit interview after hospital birth is challenging, and women generally reported 100% respectful care for themselves and their baby. Specific questions, with stratification by mode of birth, women’s age and ethnicity, are important to identify those mistreated during care and to prioritise action. More research is needed to develop evidence-based measures to track experience of care, including zero separation for the mother-newborn pair, and to improve monitoring.

Uterotonics for prevention of postpartum haemorrhage: EN-BIRTH multi-country validation study

BackgroundPostpartum haemorrhage (PPH) is a leading cause of preventable maternal mortality worldwide. The World Health Organization (WHO) recommends uterotonic administration for every woman after birth to prevent PPH. There are no standardised data collected in large-scale measurement platforms. The Every Newborn Birth Indicators Research Tracking in Hospitals (EN-BIRTH) is an observational study to assess the validity of measurement of maternal and newborn indicators, and this paper reports findings regarding measurement of coverage and quality for uterotonics.MethodsThe EN-BIRTH study took place in five hospitals in Bangladesh, Nepal and Tanzania, from July 2017 to July 2018. Clinical observers collected tablet-based, time-stamped data. We compared observation data for uterotonics to routine hospital register-records and women’s report at exit-interview survey. We analysed the coverage and quality gap for timing and dose of administration. The register design was evaluated against gap analyses and qualitative interview data assessing the barriers and enablers to data recording and use.ResultsObserved uterotonic coverage was high in all five hospitals (> 99%, 95% CI 98.7–99.8%). Survey-report underestimated coverage (79.5 to 91.7%). “Don’t know” replies varied (2.1 to 14.4%) and were higher after caesarean (3.7 to 59.3%). Overall, there was low accuracy in survey data for details of uterotonic administration (type and timing). Register-recorded coverage varied in four hospitals capturing uterotonics in a specific column (21.6, 64.5, 97.6, 99.4%). The average coverage measurement gap was 18.1% for register-recorded and 6.0% for survey-reported coverage. Uterotonics were given to 15.9% of women within the “right time” (1 min) and 69.8% within 3 min. Women’s report of knowing the purpose of uterotonics after birth ranged from 0.4 to 64.9% between hospitals. Enabling register design and adequate staffing were reported to improve routine recording.ConclusionsRoutine registers have potential to track uterotonic coverage – register data were highly accurate in two EN-BIRTH hospitals, compared to consistently underestimated coverage by survey-report. Although uterotonic coverage was high, there were gaps in observed quality for timing and dose. Standardisation of register design and implementation could improve data quality and data flow from registers into health management information reporting systems, and requires further assessment.