bluuebunny/crossref_metadata_2025_split huggingface dataset:
Expansion of testing, isolation, quarantine, e-health and telemonitoring strategies in socioeconomically vulnerable neighbourhoods at primary healthcare in the fight against COVID-19 in Brazil: a study protocol of a multisite testing intervention using a mixed method approach
Authors: Magno Laio , Rossi Thais Regis Aranha , Castanheira Débora , Torres Thiago S , Santos Carina Carvalho dos , Soares Fabiane , Veloso Valdiléa G , Benedetti Marcos , Dourado Ines 
Published: June 2023

IntroductionThe key tools for mitigating the impact of COVID-19 and reducing its transmission include testing, quarantine and isolation, as well as telemonitoring. Primary healthcare (PHC) can be essential in increasing access to these tools. Therefore, the primary objective of this study is to implement and expand an intervention consisting of COVID-19 testing, isolation, quarantine and telemonitoring (TQT) strategies and other prevention measures at PHC services in highly socioeconomically vulnerable neighbourhoods of Brazil.Methods and analysisThis study will implement and expand COVID-19 testing in PHC services in two large Brazilian capital cities: Salvador and Rio de Janeiro. Qualitative formative research was conducted to understand the testing context in the communities and at PCH services. The TQT strategy was structured in three subcomponents: (1) training and technical support for tailoring the work processes of health professional teams, (2) recruitment and demand creation strategies and (3) TQT. To evaluate this intervention, we will conduct an epidemiological study with two stages: (1) a cross-sectional sociobehavioural survey among individuals from these two communities covered by PHC services, presenting symptoms associated with COVID-19 or being a close contact of a patient with COVID-19, and (2) a cohort of those who tested positive, collecting clinical data.Ethics and disseminationThe WHO Ethics Research Committee (ERC) (#CERC.0128A and #CERC.0128B) and each city’s local ERC approved the study protocol (Salvador, ISC/UFBA: #53844121.4.1001.5030; and Rio de Janeiro, INI/Fiocruz: #53844121.4.3001.5240, ENSP/Fiocruz: #53844121.4.3001.5240 and SMS/RJ #53844121.4.3002.5279). Findings will be published in scientific journals and presented at meetings. In addition, informative flyers and online campaigns will be developed to communicate study findings to participants, members of communities and key stakeholders.
Disabled people’s perceptions and experiences of accessing and receiving counselling and psychotherapy: a scoping review protocol
Authors: Longhurst Phaedra , Full Wayne 
Published: June 2023

IntroductionEvidence indicates that, compared with their non-disabled counterparts, disabled people are likely to face greater mental health challenges as well as significant inequalities in accessing appropriate therapeutic support. Currently, little is known about how disabled people perceive and experience counselling and psychotherapy, what barriers/facilitators to therapy delivery and/or therapy participation exist for disabled clients and whether clinicians sufficiently adapt their practice to meet the needs of this diverse but marginalised population. In this paper, we outline a proposal for undertaking a scoping review that aims to identify and synthesise current research relating to disabled individuals’ perceptions of accessibility and experiences of counselling and psychotherapy. The review aims to identify current gaps in the evidence base and inform how future research, practice and policy may develop and foster inclusive strategies and approaches which will support the psychological well-being of disabled clients accessing counselling and psychotherapy.Methods and analysisThe undertaking and reporting of the proposed scoping review will be guided by the framework outlined by Arksey and O’Malley and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. Systematic searches of the PsycINFO, CINAHL, EMBASE, EBSCO and Cochrane Library electronic databases will be conducted. Reference lists of relevant studies will be reviewed to identify additional studies. Eligible studies will be limited to those published from 1 January 2010 to 31 December 2022 and in the English language. Empirical studies involving disabled individuals receiving and/or who have received a form of therapeutic intervention will be included. Data will be extracted, collated and charted, and will be summarised quantitatively through descriptive numerical analysis and qualitatively through a narrative synthesis.Ethics and disseminationThe proposed scoping review of published research will not require ethical approval. Results will be disseminated through publication in a peer-reviewed journal.
Factors associated with self-care behaviours among people with hypertension residing in Kathmandu: a cross-sectional study
Authors: Nakarmi Chandani Singh , Uprety Samyog , Ghimire Anup , Chakravartty Avaniendra , Adhikari Bikram , Khanal Niharika , Dahal Sitasnu , Mali Sushmita , Pyakurel Prajjwal 
Published: June 2023

ObjectiveTo determine the prevalence and associated factors of self-care behaviours among people with hypertension in the Kathmandu district of Nepal.DesignCross-sectional study.SettingMunicipalities of Kathmandu district, Nepal.ParticipantsWe enrolled 375 adults aged ≥18 years with a minimum 1-year duration of hypertension using multistage sampling.Outcome measuresWe used the Hypertension Self-care Activity Level Effects to assess self-care behaviours and collected data through face-to-face interviews. We conducted univariate and multivariable logistic regression analyses to determine the factors associated with self-care behaviours. The results were summarised as crude and adjusted ORs (AORs) with 95% CIs.ResultsThe adherence to antihypertensive medication, Dietary Approach to Stop Hypertension (DASH) diet, physical activity, weight management, alcohol moderation, and non-smoking were 61.3%, 9.3%, 59.2%, 14.1%, 90.9%, and 72.8%, respectively. Secondary or higher education (AOR: 4.42, 95% CI: 1.11 to 17.62), Brahmin and Chhetri ethnic groups (AOR: 3.30, 95% CI: 1.26 to 8.59) and good to very good perceived health (AOR: 3.96, 95% CI: 1.60 to 9.79) were positively associated with DASH diet adherence. Males (AOR: 2.05, 95% CI: 1.19 to 3.55) had higher odds of physical activity. Brahmin and Chhetri ethnic groups (AOR: 3.44, 95% CI: 1.63 to 7.26) and secondary or higher education (AOR: 4.70, 95% CI: 1.62 to 13.63) were correlates of weight management. Secondary or higher education (AOR: 2.47, 95% CI: 1.16 to 5.29), body mass index ≥25 kg/m2(AOR: 1.83, 95% CI: 1.04 to 3.22) and income above the poverty line (AOR: 2.24, 95% CI: 1.08 to 4.63) were positively associated with non-smoking. Furthermore, Brahmin and Chhetri ethnic groups (AOR: 4.51, 95% CI: 1.64 to 12.40), males (AOR: 0.17, 95% CI: 0.06 to 0.50) and primary education (AOR: 0.26, 95% CI: 0.08 to 0.85) were associated with alcohol moderation.ConclusionThe adherence to the DASH diet and weight management was particularly low. Healthcare providers and policymakers should focus on improving self-care by designing simple and affordable interventions for all patients with hypertension.
Project period: Improving period product provision in a large inner city hospital
Authors: Knox Laura , Thornton Philippa , McNee Rosie , Bough Isabelle , Rodgers Daniel 
Published: June 2023

BackgroundBMA guidance recommends all hospitals provide suitable, comfortable and convenient period products. In 2018, none of Scotland’s health boards had policies on sanitary product provision.AimEstablish current provision at Glasgow Royal Infirmary.Improve provision for staff and patients.Improve working experience for staff while menstruating.MethodsCycle 0: A pilot survey was circulated to assess current provision, availability and impact on working environment.Cycle 1: The survey was circulated to all hospital staff. Suppliers were contacted for donations. Two menstrual hubs were established in the medical receiving unit.Cycle 2: Polling confirmed the preferred mode of downstream product distribution, with products supplied to each ward. Menstrual hub use was monitored.Cycle 3: Staff were resurveyed following the interventions. Findings were presented to hospital and board managers.ResultsCycle 0: 95% felt current provision for staff was not appropriate. 77% felt provisions were inappropriate for patients (n=22).Cycle 1: 97% felt hospital period product provisions were not appropriate. 84% of menstruators had no access to products when required: 55% asked colleagues for products; 50% used makeshift products and 8% used hospital pads. Overall, 84% did not know where to access period products within the hospital (n=968).Cycle 2: 91% preferred ward-based provisions (n=46).Cycle 3: 95% agreed that project period products were suitable for their needs (n=71). 82% felt access to period products had improved for personal use and 47% for patients. 58% were able to locate products for staff and 49% for patients.ConclusionProject period highlighted a need for menstrual product provision in hospitals. It increased knowledge, suitability and availability of period products, and created a robust model of provision which may be easily replicated.
Development and evaluation of a continuous quality improvement programme for antimicrobial stewardship in six hospitals in Uganda
Authors: Kiggundu Reuben , Waswa JP , Nakambale Hilma N , Kakooza Francis , Kassuja Hassan , Murungi Marion , Akello Harriet , Morries Seru , Joshi Mohan P , Stergachis Andy , Konduri Niranjan 
Published: June 2023

BackgroundAppropriate antimicrobial use is essential for antimicrobial stewardship (AMS). Ugandan hospitals are making efforts to improve antibiotic use, but improvements have not been sufficiently documented and evaluated.MethodsSix Ugandan hospitals implemented AMS interventions between June 2019 and July 2022. We used the WHO AMS toolkit to set-up hospital AMS programmes and implemented interventions using continuous quality improvement (CQI) techniques and targeting conditions commonly associated with antibiotic misuse, that is, urinary tract infections (UTIs), upper respiratory tract infections (URTIs) and surgical antibiotic prophylaxis (SAP). The interventions included training, mentorship and provision of clinical guidelines to support clinical decision-making. Quarterly antibiotic use surveys were conducted.ResultsData were collected for 7037 patients diagnosed with UTIs. There was an increase in the proportion of patients receiving one antibiotic for the treatment of UTI from 48% during the pre-intervention to 73.2%, p<0.01. There was a 19.2% reduction in the number of antimicrobials per patient treated for UTI p<0.01. There was an increase in use of nitrofurantoin, the first-line drug for the management of UTI. There was an increase in the use of Access antibiotics for managing UTIs from 50.4% to 53.8%. The proportion of patients receiving no antimicrobials for URTI increased from 26.3% at pre-intervention compared with 53.4% at intervention phase, p<0.01. There was a 20.7% reduction in the mean number of antimicrobials per patient for URTI from the pre-intervention to the intervention phase, from 0.8 to 0.6, respectively, p<0.001 and reduction in the number of treatment days, p=0.0163. Among patients undergoing surgery, 49.5% (2212) received SAP during the pre-intervention versus 50.5% (2169) during the intervention.ConclusionsUsing CQI approaches to focus on specific causes of inappropriate antibiotic use led to desirable overall reductions in antibiotic use for URTI and UTI.
Parallel Strong Connectivity Based on Faster Reachability
Authors: Wang Letong , Dong Xiaojun , Gu Yan , Sun Yihan 
Published: June 2023

Computing strongly connected components (SCC) is among the most fundamental problems in graph analytics. Given the large size of today's real-world graphs, parallel SCC implementation is increasingly important. SCC is challenging in the parallel setting and is particularly hard on large-diameter graphs. Many existing parallel SCC implementations can be even slower than Tarjan's sequential algorithm on large-diameter graphs.
To tackle this challenge, we propose an efficient parallel SCC implementation using a new parallel reachability approach. Our solution is based on a novel idea referred to as vertical granularity control (VGC). It breaks the synchronization barriers to increase parallelism and hide scheduling overhead. To use VGC in our SCC algorithm, we also design an efficient data structure called the parallel hash bag. It uses parallel dynamic resizing to avoid redundant work in maintaining frontiers (vertices processed in a round).
We implement the parallel SCC algorithm by Blelloch et al. (J. ACM, 2020) using our new parallel reachability approach. We compare our implementation to the state-of-the-art systems, including GBBS, iSpan, Multi-step, and our highly optimized Tarjan's (sequential) algorithm, on 18 graphs, including social, web, k-NN, and lattice graphs. On a machine with 96 cores, our implementation is the fastest on 16 out of 18 graphs. On average (geometric means) over all graphs, our SCC is 6.0× faster than the best previous parallel code (GBBS), 12.8× faster than Tarjan's sequential algorithms, and 2.7× faster than the best existing implementation on each graph.
We believe that our techniques are of independent interest. We also apply our parallel hash bag and VGC scheme to other graph problems, including connectivity and least-element lists (LE-lists). Our implementations improve the performance of the state-of-the-art parallel implementations for these two problems.
ForestTI: A Scalable Inverted-Index-Oriented Timeseries Management System with Flexible Memory Efficiency
Authors: Wang Zhiqi , Shao Zili 
Published: June 2023

Timeseries management systems play an important role in IoT and performance monitoring. As the data volume scales up, absorbing data memory efficiently with high throughput becomes a growing requirement for timeseries management systems. However, the designs of the existing systems, especially the in-memory data structures, suffer from two issues. First, they suffer from the trade-off between memory efficiency and performance. Second, they are not scalable because of lock contention where they cannot benefit from parallel insertion and querying.
In this paper, we propose ForestTI, a scalable inverted-index-oriented timeseries management system where the balance point between memory efficiency and performance can be flexibly adjusted under the increasing memory pressure. First, we present a two-level inverted index, which is scalable with optimistic lock coupling, and its internal structure can be gradually converted to more memory efficient representations. Second, we propose a two-level pointer swizzling mechanism to actively swap out the cold posting lists and in-memory timeseries objects as the number of timeseries increases. Finally, we further optimize the on-disk data structures (i.e. write-ahead logs and LSM-tree) to adapt to the high insertion throughput from the in-memory components. We prototype ForestTI with C++ from scratch, and compared to the storage engine of Prometheus, ForestTI achieves 1.79x higher insertion throughput, 52.1% lower query latency, and 56.9% lower memory occupation. We have released the open-source code of ForestTI for public access.
Practical Differentially Private and Byzantine-resilient Federated Learning
Authors: Xiang Zihang , Wang Tianhao , Lin Wanyu , Wang Di 
Published: June 2023

Privacy and Byzantine resilience are two indispensable requirements for a federated learning (FL) system. Although there have been extensive studies on privacy and Byzantine security in their own track, solutions that consider both remain sparse. This is due to difficulties in reconciling privacy-preserving and Byzantine-resilient algorithms.
In this work, we propose a solution to such a two-fold issue. We use our version of differentially private stochastic gradient descent (DP-SGD) algorithm to preserve privacy and then apply our Byzantine-resilient algorithms. We note that while existing works follow this general approach, an in-depth analysis on the interplay between DP and Byzantine resilience has been ignored, leading to unsatisfactory performance. Specifically, for the random noise introduced by DP, previous works strive to reduce its seemingly detrimental impact on the Byzantine aggregation. In contrast, we leverage the random noise to construct a first-stage aggregation that effectively rejects many existing Byzantine attacks. Moreover, based on another property of our DP variant, we form a second-stage aggregation which provides a final sound filtering. Our protocol follows the principle of co-designing both DP and Byzantine resilience.
We provide both theoretical proof and empirical experiments to show our protocol is effective: retaining high accuracy while preserving the DP guarantee and Byzantine resilience. Compared with the previous work, our protocol 1) achieves significantly higher accuracy even in a high privacy regime; 2) works well even when up to 90% distributive workers are Byzantine.
A Step Toward Deep Online Aggregation
Authors: Sheoran Nikhil , Chockchowwat Supawit , Chheda Arav , Wang Suwen , Verma Riya , Park Yongjoo 
Published: June 2023

For exploratory data analysis, it is often desirable to know what answers you are likely to get before actually obtaining those answers. This can potentially be achieved by designing systems to offer the estimates of a data operation result-say op(data)-earlier in the process based on partial data processing. Those estimates continuously refine as more data is processed and finally converge to the exact answer. Unfortunately, the existing techniques-called Online Aggregation (OLA)-are limited to a single operation; that is, we cannot obtain the estimates for op(op(data)) or op(...(op(data))). If this Deep OLA becomes possible, data analysts will be able to explore data more interactively using complex cascade operations.
In this work, we take a step toward Deep OLA with evolving data frames (edf), a novel data model to offer OLA for nested ops-op(...(op(data)))-by representing an evolving structured data (with converging estimates) that is closed under set operations. That is, op(edf) produces yet another edf; thus, we can freely apply successive operations to edf and obtain an OLA output for each op. We evaluate its viability with Wake, an edf-based OLA system, by examining against state-of-the-art OLA and non-OLA systems. In our experiments on TPC-H dataset, Wake produces its first estimates 4.93× faster (median)-with 1.3× median slowdown for exact answers-compared to conventional systems. Besides its generality, Wake is also 1.92× faster (median) than existing OLA systems in producing estimates of under 1% relative errors.
PrivLava: Synthesizing Relational Data with Foreign Keys under Differential Privacy
Authors: Cai Kuntai , Xiao Xiaokui , Cormode Graham 
Published: June 2023

Answering database queries while preserving privacy is an important problem that has attracted considerable research attention in recent years. A canonical approach to this problem is to use synthetic data. That is, we replace the input database R with a synthetic database R* that preserves the characteristics of R, and use R* to answer queries. Existing solutions for relational data synthesis, however, either fail to provide strong privacy protection, or assume that R contains a single relation. In addition, it is challenging to extend the existing single-relation solutions to the case of multiple relations, because they are unable to model the complex correlations induced by the foreign keys. Therefore, multi-relational data synthesis with strong privacy guarantees is an open problem.
In this paper, we address the above open problem by proposing PrivLava, the first solution for synthesizing relational data with foreign keys under differential privacy, a rigorous privacy framework widely adopted in both academia and industry. The key idea of PrivLava is to model the data distribution in R using graphical models, with latent variables included to capture the inter-relational correlations caused by foreign keys. We show that PrivLava supports arbitrary foreign key references that form a directed acyclic graph, and is able to tackle the common case when R contains a mixture of public and private relations. Extensive experiments on census data sets and the TPC-H benchmark demonstrate that PrivLava significantly outperforms its competitors in terms of the accuracy of aggregate queries processed on the synthetic data.
GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data
Authors: Chai Chengliang , Liu Jiabin , Tang Nan , Fan Ju , Miao Dongjing , Wang Jiayi , Luo Yuyu , Li Guoliang 
Published: June 2023

Given a dataset with incomplete data (e.g., missing values), training a machine learning model over the incomplete data requires two steps. First, it requires a data-effective step that cleans the data in order to improve the data quality (and the model quality on the cleaned data). Second, it requires a data-efficient step that selects a core subset of the data (called coreset) such that the trained models on the entire data and the coreset have similar model quality, in order to improve the training efficiency. The first-data-effective-then-data-efficient methods are too costly, because they are expensive to clean the whole data; while the first-data-efficient-then-data-effective methods have low model quality, because they cannot select high-quality coreset for incomplete data.
In this paper, we investigate the problem of coreset selection over incomplete data for data-effective and data-efficient machine learning. The essential challenge is how to model the incomplete data for selecting high-quality coreset. To this end, we propose the GoodCore framework towards selecting a good coreset over incomplete data with low cost. To model the unknown complete data, we utilize the combinations of possible repairs as possible worlds of the incomplete data. Based on possible worlds, GoodCore selects an expected optimal coreset through gradient approximation without training ML models. We formally define the expected optimal coreset selection problem, prove its NP-hardness, and propose a greedy algorithm with an approximation ratio. To make GoodCore more efficient, we further propose optimization methods that incorporate human-in-the-loop imputation or automatic imputation method into our framework. Experimental results show the effectiveness and efficiency of our framework with low cost.