Ranking Data Research Articles

Shared Autonomy to Reduce Sedentary Behavior Among Sit-Stand Desk Users in the United States and India: Web-Based Study.

Fitness technologies such as wearables and sit-stand desks are increasingly being used to fight sedentary lifestyles by encouraging physical activity. However, adherence to such technologies decreases over time because of apathy and increased dismissal of behavioral nudges. To address this problem, we introduced shared autonomy in the context of sit-stand desks, where user input is integrated with robot autonomy to control the desk and reduce sedentary behavior and investigated user reactions and preferences for levels of automation with a sit-stand desk. As demographics affect user acceptance of robotic technology, we also studied how perceptions of nonvolitional behavior change differ across cultures (United States and India), sex, familiarity, dispositional factors, and health priming messages. We conducted a web-based vignette study in the United States and India where a total of 279 participants watched video vignettes of a person interacting with sit-stand desks of various levels of automation and answered questions about their perceptions of the desks such as ranking of the different levels of automation. Participants generally preferred either manual or semiautonomous desks over the fully autonomous option (P<.001). However, participants in India were generally more amenable to the idea of nonvolitional interventions from the desk than participants in the United States (P<.001). Male participants had a stronger desire for having control over the desk than female participants (P=.01). Participants who were more familiar with sit-stand desks were more likely to adopt autonomous sit-stand desks (P=.001). No effects of health priming messages were observed. We estimated the projected health outcome by combining ranking data and hazard ratios from previous work and found that the semiautonomous desk led to the highest projected health outcome. These results suggest that the shared autonomy desk is the optimal level of automation in terms of both user preferences and estimated projected health outcomes. Demographics such as culture and sex had significant effects on how receptive users were to autonomous intervention. As familiarity improves the likelihood of adoption, we propose a gradual behavior change intervention to increase acceptance and adherence, especially for populations with a high desire for control.

Abstract 183: Prioritizing Interventions To Reduce The Caregiver Burden Among Racially And Ethnically Diverse Co-survivors Of Cardiac Arrest Survivors: A Cross-sectional National Survey Study

Introduction: Cardiac arrest (CA) co-survivors i.e., close family members of CA survivors, have high levels of caregiver burden and poor quality of life. We aim to prioritize potential patient-, and co-survivor-centric interventions that may alleviate caregiver burden. Methods: We distributed an online survey to adult, co-survivor members of the Sudden Cardiac Arrest Foundation and Parent Heart Watch national databases and CA-specific Facebook groups. Participants provided demographics, details on their loved ones’ CA, and their caregiving role. They then ranked their top choice among 8 unique interventions focusing on either patient-centric education-based needs (n=4) or co-survivor-centric resources for self-care and enhanced psychological support (n=4). The survey was developed by a multidisciplinary research team and patient and co-survivor stakeholders. Results: Of 657 responses received, 550 co-survivors (57% were between the age of 18-40 years, 65% were women, 51% of minority race/ethnicity, 53% partners or spouse, an average of 8 hours of caregiving, for an average of 5 months) with the complete ranking data, were analyzed. A total of 347 (63%) co-survivors ranked patient-centric education-based interventions as their top choice ( Figure 1 shows distributions ). A multivariate model estimating the drivers of preferences for education-based interventions over co-survivors resource-based interventions showed age&gt;40 years, direct discharge to home after CA vs inpatient rehabilitation, co-survivor witnessing the CA, or assuming the caregiver role during hospitalization or within 1-month of CA, were significant predisposing factors. Conclusions: Education-based interventions, particularly information related to their loved ones’ recovery, are the top priorities for co-survivors during the early phase of CA survivorship. Future studies should focus on developing and testing these interventions for alleviation of caregiver burden.

Reasons and solutions for unhealthy food consumption and physical inactivity among school-going adolescents: A sequential mixed-methods study in Puducherry, South India.

Unhealthy food consumption and physical inactivity in adolescents play a key role in the development of non-communicable diseases (NCDs). Objectives: The study was planned to capture the reasons and solutions for unhealthy food consumption and physical inactivity among school-going adolescents. A school-based sequential mixed-methods study was conducted in Puducherry for six months. In Phase I, a survey (QUAN) was carried out among 405 representative students from 9th to 12th standards to identify the reasons for unhealthy behaviors. Then, Phase II compromised two focus group discussions (FGDs) (QUAL) with 20 purposively selected school staff, parents, and healthcare professionals to explore the solutions for unhealthy behaviors. In Phase III, the key action points were ranked (QUAL) by 60 teachers. Quantitative data was analyzed in Epi_Info 7.1.5.0 software (Centers for Disease Control and Prevention; Atlanta, Georgia, US). Thematic content analysis was done for the qualitative data in Atlas.ti.9 software (Scientific Software Development GmbH, Berlin). Further, mean rank and Kendall's concordance coefficient (W) were calculated for the ranked data in SPSS 24 software (SPSS Inc; Chicago, Illinois, USA). About 70.1% of students had unhealthy food consumption and 61% of students lacked physical activity. Notably, 59.9% of males preferred unhealthy foods, and 65.2% of females were physically inactive. The leading reasons for unhealthy eating habits were taste (78.9%), increased online food delivery (75.7%), and attractive advertisements (74.3%). Whereas, the prime reasons for sedentary behaviors were increased study load (81.8%), high-density traffic (74.9%), and insufficient recreational facilities (71.7%). The prioritized feasible action points would help in the development of context-specific behavior change communication strategies for future health promotion interventions in resource-poor settings.

PERANCANGAN SISTEM INFORMASI DATA APARATUR SIPIL NEGARA BERDASARAKAN URUTAN KEPANGKATAN BERBASIS WEB DI BAPPEDA KOTA JAMBI

At the Bappeda Office (Regional Development Planning Agency) Jambi City has 75 ASN (State Civil Apparatus) employees. So far, in the process of processing employee data, it is still in the conventional way with Microsoft Excel office applications and the data is not yet centralized, it is still scattered in each field, to obtain information or edit employee data, especially the focus of this research is on rank data information which is constantly available. changes, to obtain this information, it must take 2 to 3 days because they have to collect data from their respective fields, then the data is submitted to the Head of the Sub-Division of Personnel and Households, this makes the author think of creating an information system that is realtime and efficient in order to facilitate the performance of a Head of Sub-Division of Personnel and Household. System design is done by using DFD and ERD to model data and processes. With this system, it is hoped that it will make it easier for all ASN employees within the Jambi City Bappeda and related and interested agencies who have access to get the latest information.

Discrete Correlation Summation Clustering Reveals Differential Regulation of Liver Metabolism by Thrombospondin-1 in Low-Fat and High-Fat Diet-Fed Mice.

Thrombospondin-1 (TSP1) is a matricellular protein with many important roles in mediating carcinogenesis, fibrosis, leukocyte recruitment, and metabolism. We have previously shown a role of diet in the absence of TSP1 in liver metabolism in the context of a colorectal cancer model. However, the metabolic implications of TSP1 regulation by diet in the liver metabolism are currently understudied. Therefore Discrete correlation summation (DCS) was used to re-interrogate data and determine the metabolic alterations of TSP1 deficiency in the liver, providing new insights into the role of TSP1 in liver injury and the progression of liver pathologies such as nonalcoholic fatty liver disease (NAFLD). DCS analysis provides a straightforward approach to rank covariance and data clustering when analyzing complex data sets. Using this approach, our previous liver metabolite data was re-analyzed by comparing wild-type (WT) and Thrombospondin-1 null (Thbs1-/-) mice, identifying changes driven by genotype and diet. Principal component analysis showed clustering of animals by genotype regardless of diet, indicating that TSP1 deficiency alters metabolite handling in the liver. High-fat diet consumption significantly altered over 150 metabolites in the Thbs1-/- livers versus approximately 90 in the wild-type livers, most involved in amino acid metabolism. The absence of Thbs1 differentially regulated tryptophan and tricarboxylic acid cycle metabolites implicated in the progression of NAFLD. Overall, the lack of Thbs1 caused a significant shift in liver metabolism with potential implications for liver injury and the progression of NAFLD.

Using GIS to Identify Pedestrian-Vehicle Crash Hot Spots and Unsafe Bus Stops

This paper presents a GIS approach based on spatial autocorrelation analysis of pedestrian-vehicle crash data for identification and ranking of unsafe bus stops. Instead of crash counts, severity indices are used for analysis and ranking. Moran’s I statistic is employed to examine spatial patterns of pedestrian-vehicle crash data. Getis-Ord Gi✽ statistic is used to identify the clustering of low and high index values and to generate a pedestrian-vehicle crash hot spots map. As recent studies have shown strong correlations between pedestrian-vehicle crashes and transit access, especially bus stops, bus stops in pedestrian-vehicle crash hot spots are then selected and ranked based on the severity of pedestrian-vehicle crashes in their vicinities. The proposed approach is evaluated using 13 years (1996–2008) of pedestrian-vehicle crash data for the Adelaide metropolitan area. Results show that the approach is efficient and reliable in identifying pedestrian-vehicle crash hot spots and ranking unsafe bus stops.

Systematic comparison of ranking aggregation methods for gene lists in experimental results.

A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The gene lists resulting from a group of studies answering the same, or similar, questions can be combined by ranking aggregation methods to find a consensus or a more reliable answer. Evaluating a ranking aggregation method on a specific type of data before using it is required to support the reliability since the property of a dataset can influence the performance of an algorithm. Such evaluation on gene lists is usually based on a simulated database because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. In this study, a group of existing methods and their variations that are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data were used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomic data, with various heterogeneity of quality, noise level and a mix of unranked and ranked data using 20000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (non-small cell lung cancer) and bacteria (macrophage apoptosis) was performed. We summarize the results of our evaluation in a simple flowchart to select a ranking aggregation method, and in an automated implementation using the meta-analysis by information content algorithm to infer heterogeneity of data quality across input datasets. The code for simulated data generation and running edited version of algorithms: https://github.com/baillielab/comparison_of_RA_methods. Code to perform an optimal selection of methods based on the results of this review, using the MAIC algorithm to infer the characteristics of an input dataset, can be downloaded here: https://github.com/baillielab/maic. An online service for running MAIC: https://baillielab.net/maic. Supplementary data are available at Bioinformatics online.

Nonparametric Analysis of Balanced Incomplete Block Rank Data

Traditional nonparametric analysis of balanced incomplete block rank data usually involves the Durbin test. Here we give an alternative adjustment for the Durbin statistic for when there are ties and mid-ranks are used. The adjusted Durbin statistic is shown to be simply related to the ANOVA F statistic on the ranks, so that the corresponding tests are, in a sense, equivalent. This means both are tests of equality of mean treatment ranks. A simulation study compares the size and power performances of the competing tests. We also apply the nonparametric ANOVA methodology to give tests for univariate moment effects and, when treatments are ordered, for bivariate moment effects. The latter includes umbrella tests.

"The reputation premium": does hospital ranking improvement lead to a higher healthcare spending?

Global health systems have often disclosed hospital quality and performance information via hospital ranking or rating programs over the last 20years. This study aims to examine the relationship between hospital ranking and healthcare spending. Using the Basic Medical Insurance claims data from a big city in central China and the hospital ranking data from the Fudan Chinese Hospital League Table from 2016 to 2018, this study exploits the variation of hospital reputable ranking across hospitals and periods to employ the difference-in-differences (DiD) design. To alleviate the self-selection bias emerging from inpatients' selection of hospitals and the extrapolation bias emerging from the potential mis-specification of our linear model, we combine the DiD design with the 3-to-1 optimal Mahalanobis metric matching method. This study finds that ceteris paribus one hospital ascending from the Regional Famous Hospital Group to the National Famous Hospital Group significantly increases inpatients' total healthcare costs, reimbursement costs, and out-of-pocket costs by 5.9%, 6.2%, and 4.0%, respectively. Mechanism analysis reveals that it should be attributed more to physician moral hazard than patient willingness-to-pay. Leads and lags (event study) analysis validates our DiD identification framework and shows that the impact materializes slowly but significantly. In the robustness check, we transfer the outcome variables from the log value to the level value and control five digits of ICD-10 for the disease fixed-effects. The results are highly robust.

Development of Kapiten Coffe as a Media to Branding an Agropolitan City: Case Study in Pasuruan, Indonesia

The concept of commercialization is a strategic step in introducing Kapiten coffee products to be the flagship of Pasuruan Regency and brands with measurable coffee quality. This commercialization involves three important actors; government, academics and entrepreneurs (triple helix) so that the captain's coffee succeeded in bringing Pasuruan Regency. This study uses a non-metric Multidimensional Scaling method with ranking data processing. The method used is that data is obtained through preference ranking, where the steps are as follows: making a questionnaire, ranking various brands based on existing attributes, then mapping the attributes. Data on this research take from some of coffe brand in East Java Province using case study. In addition, this study also uses the Critical Path Method which is used to measure the duration of the work. This method will greatly help project managers in analyzing, planning and scheduling projects more efficiently. The results of this study indicate that Kapiten coffee is superior in terms of taste, soft texture to attractive packaging, which is an important strategy to introduce Kapiten coffee products into the free market; both domestically and internationally. As for the captain's coffee, it takes 51 days to be widely marketed. Kapiten coffee has advantages and tastes, textures and packaging that are distinctive from other coffees. So this is a special attraction for consumers.

Tissue-selecting-technique mega-window stapler combined with anal canal epithelial preservation operation in prolapsed hemorrhoids

ObjectiveThis study aimed to compare the efficiency of these two combined surgeries [prolapse and hemorrhoids (PPH) combined with the external hemorrhoidectomy and inferior internal hemorrhoid ligation; tissue selecting technique with mega-window stapler (TST-MS) combined with anal canal epithelial preservation operation]. MethodsThis is a single-center, evaluator-blinded randomized controlled trial (RCT). A total of 204 participants were randomly divided into the two groups. The anal function, reoccurrence, intraoperative variables, and operative complications were assessed. The measurement data were compared by paired t test and rank sum test. Chi-squared or Fisher's exact test was used for count data and rank sum test for ranked data. To assess differences within each group in different time points, the repeated-measures analysis of variance was conducted. ResultsTST-MS combined with anal canal epithelial preservation operation had lower Wexner score of anal incontinence (Z = 3.062, P = 0.002), higher patients’ satisfaction degree (t = 7.32, P < 0.001), less residual skin tags (χ2 = 18.141, P < 0.001), longer operative time (Z = −2.281, P = 0.023), and bigger volume of excised rectal mucosa (t = 2.35, P = 0.020). There was no significant difference between the two groups in the anal canal circumference, recurrence, intraoperative blood loss, weight of excised rectal mucosa, anal pain, bleeding, urinary retention, anal edge edema, and anatomical anal stenosis (all P > 0.05). ConclusionsTST-MS combined with anal canal epithelial preservation operation showed better clinical efficiency in the treatment of prolapsed hemorrhoids. Trial registrationThis study has been registered in Chinese Clinical Trial Registry (ChiCTR2000038533)

Blue Skies With Clouds: Envisioning the Future Ideal State and Identifying Ongoing Tensions in the UME-GME Transition.

The transition from medical school to residency in the United States consumes large amounts of time for students and educators in undergraduate and graduate medical education (UME, GME), and it is costly for both students and institutions. Attempts to improve the residency application and Match processes have been insufficient to counteract the very large number of applications to programs. To address these challenges, the Coalition for Physician Accountability charged the Undergraduate Medical Education to Graduate Medical Education Review Committee (UGRC) with crafting recommendations to improve the system for the UME-GME transition. To guide this work, the UGRC defined and sought stakeholder input on a "blue-skies" ideal state of this transition. The ideal state views the transition as a system to support a continuum of professional development and learning, thus serving learners, educators, and the public, and engendering trust among them. It also supports the well-being of learners and educators, promotes diversity, and minimizes bias. This manuscript uses polarity thinking to analyze 3 persistent key tensions in the system that require ongoing management. First, the formative purpose of assessment for learning and growth is at odds with the use of assessment data for ranking and sorting candidates. Second, the function of residents as learners can conflict with their role as workers contributing service to health care systems. Third, the current residency Match process can position the desire for individual choice-among students and their programs-against the workforce needs of the profession and the public. This Scholarly Perspective presents strategies to balance the upsides and downsides inherent to these tensions. By articulating the ideal state of the UME-GME transition and anticipating tensions, educators and educational organizations can be better positioned to implement UGRC recommendations to improve the transition system.

Markov Chain Monte Carlo for generating ranked textual data

This paper faces a central theme in applied statistics and information science, which is the assessment of the stochastic structure of rank-size laws in text analysis. We consider the words in a corpus by ranking them on the basis of their frequencies in descending order. The starting point is that the ranked data generated in linguistic contexts can be viewed as the realisations of a discrete states Markov chain, whose stationary distribution behaves according to a discretisation of the best fitted rank-size law. The employed methodological toolkit is Markov Chain Monte Carlo, specifically referring to the Metropolis–Hastings algorithm. The theoretical framework is applied to the rank-size analysis of the hapax legomena occurring in the speeches of the US Presidents. We offer a large number of statistical tests leading to the consistency of our methodological proposal. To pursue our scopes, we also offer arguments supporting that hapaxes are rare (“extreme”) events resulting from memory-less-like processes. Moreover, we show that the considered sample has the stochastic structure of a Markov chain of order one. Importantly, we discuss the versatility of the method, which is considered suitable for deducing similar outcomes for other applied science contexts.

Modelling Time-Varying Rankings with Autoregressive and Score-Driven Dynamics

AbstractWe develop a new statistical model to analyse time-varying ranking data. The model can be used with a large number of ranked items, accommodates exogenous time-varying covariates and partial rankings, and is estimated via the maximum likelihood in a straightforward manner. Rankings are modelled using the Plackett–Luce distribution with time-varying worth parameters that follow a mean-reverting time series process. To capture the dependence of the worth parameters on past rankings, we utilise the conditional score in the fashion of the generalised autoregressive score models. Simulation experiments show that the small-sample properties of the maximum-likelihood estimator improve rapidly with the length of the time series and suggest that statistical inference relying on conventional Hessian-based standard errors is usable even for medium-sized samples. In an empirical study, we apply the model to the results of the Ice Hockey World Championships. We also discuss applications to rankings based on underlying indices, repeated surveys and non-parametric efficiency analysis.

Comparative Evaluation of Clinical Performance of Three Different Glass Hybrid Restorations in High Caries Risk Patients: A Randomized Control Trial

BACKGROUND: Finding a restorative material that can survive and be quite resistant in high caries risk patients is very challenging, that’s why three different glass hybrid restorations were tested for their clinical performance for one year in high caries risk patients. AIM: This study was conducted to evaluate the clinical performance of three glass hybrid restorations; nano-ionomer restoration (Ketac Nano), bioactive ionomer glass fillers (Activa bioactive), and alkasite restorative material (Cention N) in Class I cavity in high caries risk patients after 1 year according to the FDI criteria for the assessment of dental restorations. METHODS AND MATERIALS: Fifteen cooperative patients, males or females with high caries risk who were approving to participate in the trial of age range 18–50 years, were selected in the present study. Every patient should have three or more posterior teeth having occlusal pits and fissure carious lesions. Three Class I cavities were performed for every patient and restored randomly with Ketac Nano™, Activa Bioactive™, and Cention N™. All three restorations were applied, according to the manufacturers’ instructions. Restorations were evaluated at baseline (immediately), after 3 months, 6 months, and 1 year by two blinded assessors using FDI criteria for the assessment of dental restorations measuring functional properties. STATISTICAL ANALYSIS USED: Evaluation of functional properties was checked by Kruskal–Wallis non-parametric test statistics at 0.05 level. Collected data were analyzed for descriptive statistics both graphically and mathematically in terms of frequency, percent, median, interquartile range, and mean and standard deviations. Differences in evaluations between materials (M1, M2, and M3) were carried out by Chi-squared test at 0.05 level. However, differences between follow-up times (T0, T1, T2, and T3) were carried out by Friedman’s test for related samples for non-parametric data. Variations caused by the three materials and follow-up times in addition to interaction between them were assessed by repeated measures ANOVA for ranked data at significance levels of 0.05. Data analysis was carried out using computer software Statistical Package for the Social Sciences SPSS (IBM-SPSSver.23.0 for MacOS). RESULTS: Results of functional properties of the three restorative materials revealed that at baseline (T0) and 3 months (T1), all cases (100%) of the three materials were clinically successful, with no significant difference between them. However, after 6 months, 58 cases (97%) of M1, 56 cases (93%) of M2, and 54 cases (90%) of M3 were clinically successful, with non-significant difference between them. Moreover, after 12 months, 55 cases (91.6%) of M1, 56 cases (93%) of M2, and 53 cases (88%) of M3 were clinically successful, with non-significant difference between them. CONCLUSION: In the purpose of restoring posterior permanent teeth in high caries risk patients, all three restorative materials demonstrated acceptable clinical performance in Class I cavities with the same success rate.

OpenDS4All

Over the past decade, the interest in data science careers has been unwavering. In 2001, Linkedin ranked data science specialists as one of its top jobs, and noted that hiring for these roles have grown nearly 46 percent since 2019. Yet there is still a significant data science skills gap in the market and part of the problem is education focused. Research conducted by the University of California, Riverside shows that fewer than a third of the US News &amp; World Report's Top 100 Global Universities offer degrees in Data Science, and most programs are still taught at a graduate or PhD level.

PENENTUAN SISWA PENERIMA BANTUAN PENDIDIKAN MENGGUNAKAN METODE AHP DAN SAW

In the process of determining student beneficiaries using the manual method, it can affect time efficiency and data accuracy. By using a systematic calculation using a decision support system (DSS) that combines expert methods and SAW the resulting data can be used as an alternative to determine students who receive assistance to produce accurate data. In this case, the decision support system uses criteria, namely final semester grades, parental income, certificates, number of dependents and smart indonesia cards. This system is built on a web-based basis using the PHP programming language and Mysql database. The final result of this system can display ranking data and a combination of two methods. The resulting data can be printed in the form of a report. The resulting report can be used as a recommendation in the decision-making process regarding students who are entitled to receive educational assistance.

Spatialized Analysis of Air Pollution Complaints in Beijing Using the BERT+CRF Model

(1) Background: To better carry out air pollution control and to assist in accurate investigations of air pollution, in this study, we fully explore the spatial distribution characteristics of air pollution complaint results and provide guidance for air pollution control by combining regional air monitoring data. (2) Methods: By selecting the air pollution complaint information in Beijing from 2019 to 2020, in this study, we extract the names and addresses of complaint points, as well as the complaint times and types by adopting the BERT (bidirectional encoder representations from transformers) + CRF (conditional random field) model deep learning method. Moreover, through further filtering and processing of the complaint points’ address information, we achieve address matching and spatial positioning of the complaint points, and realize the regional spatial representation of air pollution complaints in Beijing in the form of a heat map. (3) Results: The experimental results are compared and analyzed with the ranking data of total suspended particulate (TSP) concentration of townships (streets) in Beijing during the same period, indicating that the key areas of air pollution complaints have a high correlation with the key polluted township (street) areas. The distribution of complaints and the types of complaints in each township (street) differ according to the population density in each township (street), the level of education, and economic activity. (4) Conclusions: The results of this study show that the public, as the intuitive perceiver of air pollution, is sensitive to the air pollution situation at a smaller spatial scale; furthermore, complaints can provide guidance and reference for the direction of air pollution control and law enforcement investigations when coupled with geographical features and economic status.

Enhancing sustainable development goals or promoting universities? An analysis of the times higher education impact rankings

Purpose This study aims to analyse and evaluate the methodology followed by the Times Higher Education Impact Rankings (THE-IR), as well as the coverage obtained and the data offered by this ranking, to determine if its methodology reflects the degree of sustainability of universities, and whether their results are accurate enough to be used as a data source for research and strategic decision-making. Design/methodology/approach A summative content analysis of the THE-IR methodology was conducted, paying special attention to the macro-structure (university score) and micro-structure (sustainable development goals [SDG] score) levels of the research-related metrics. Then, the data published by THE-IR in the 2019, 2020 and 2021 edition was collected via web scraping. After that, all the data was statistically analysed to find out performance rates, SDGs’ success rates and geographic distributions. Finally, a pairwise comparison of the THE-IR against the Times Higher Education World University Rankings (THE-WUR) was conducted to calculate overlap measures. Findings Severe inconsistencies in the THE-IR methodology have been found, offering a distorted view of sustainability in higher education institutions, allowing different strategic actions to participate in the ranking (interested, strategic, committed and outperformer universities). The observed growing number of universities from developing countries and the absence of world-class universities reflect an opportunity for less-esteemed institutions, which might have a chance to gain reputation based on their efforts towards sustainability, but from a flawed ranking which should be avoided for decision-making. Practical implications University managers can be aware of the THE-IR validity when demanding informed decisions. University ranking researchers and practitioners can access a detailed analysis of the THE-IR to determine its properties as a ranking and use raw data from THE-IR in other studies or reports. Policy makers can use the main findings of this work to avoid misinterpretations when developing public policies related to the evaluation of the contribution of universities to the SDGs. Otherwise, these results can help the ranking publisher to improve some of the inconsistencies found in this study. Social implications Given the global audience of the THE-IR, this work contributes to minimising the distorted vision that the THE-IR projects about sustainability in higher education institutions, and alerts governments, higher education bodies and policy makers to take precautions when making decisions based on this ranking. Originality/value To the best of the authors’ knowledge, this contribution is the first providing an analysis of the THE-IR’s methodology. The faults in the methodology, the coverage at the country-level and the overlap between THE-IR and THE-WUR have unveiled the existence of specific strategies in the participation of universities, of interest both for experts in university rankings and SDGs.

Selecting intervention content to target barriers and enablers of recognition and response to deteriorating patients: an online nominal group study

BackgroundPatients who deteriorate in hospital wards without appropriate recognition and/or response are at risk of increased morbidity and mortality. Track-and-trigger tools have been implemented internationally prompting healthcare practitioners (typically nursing staff) to recognise physiological changes (e.g. changes in blood pressure, heart rate) consistent with patient deterioration, and then to contact a practitioner with expertise in management of acute/critical illness. Despite some evidence these tools improve patient outcomes, their translation into clinical practice is inconsistent internationally. To drive greater guideline adherence in the use of the National Early Warning Score tool (a track-and-trigger tool used widely in the United Kingdom and parts of Europe), a theoretically informed implementation intervention was developed (targeting nursing staff) using the Theoretical Domains Framework (TDF) version 2 and a taxonomy of Behaviour Change Techniques (BCTs).MethodsA three-stage process was followed: 1. TDF domains representing important barriers and enablers to target behaviours derived from earlier published empirical work were mapped to appropriate BCTs; 2. BCTs were shortlisted using consensus approaches within the research team; 3. shortlisted BCTs were presented to relevant stakeholders in two online group discussions where nominal group techniques were applied. Nominal group participants were healthcare leaders, senior clinicians, and ward-based nursing staff. Stakeholders individually generated concrete strategies for operationalising shortlisted BCTs (‘applications’) and privately ranked them according to acceptability and feasibility. Ranking data were used to drive decision-making about intervention content.ResultsFifty BCTs (mapped in stage 1) were shortlisted to 14 (stage 2) and presented to stakeholders in nominal groups (stage 3) alongside example applications. Informed by ranking data from nominal groups, the intervention was populated with 12 BCTs that will be delivered face-to-face, to individuals and groups of nursing staff, through 18 applications.ConclusionsA description of a theory-based behaviour change intervention is reported, populated with BCTs and applications generated and/or prioritised by stakeholders using replicable consensus methods. The feasibility of the proposed intervention should be tested in a clinical setting and the content of the intervention elaborated further to permit replication and evaluation.