Ranked List Research Articles

The Effect of Price and Security on Tourism Demand: Panel Quantile Regression Approach

It is of great importance for countries to increase the contribution of the tourism sector to the economy. Therefore, the authorities focus on how to increase the demand for tourism. However, the most important issue is to define the factors that influence the demand for tourism in a complex environment and this study attempts to contribute to this field. Specifically, the study examines the effects of REER (Real Effective Exchange Rate) and security conditions on tourism demand using panel data methods for 73 countries, in the tourism ranking list from UNWTO reports, over the period 2003-2018. The main results of this study show that while the effect of REER on tourism demand is negative, the security condition has a positive effect on the demand for the tourism sector. In addition to these findings which confirm the existing literature, the innovative character of the methodology – fixed-effect panel quantile regression analysis - allowed us to check whether the effects of these variables may vary in different percentiles of tourism demand. Estimation result reveals that the effect of change in REER on tourism demand increases in high percentiles. Nevertheless, the effect of the security on tourism demand decreases as percentiles increase.

Early skills laboratory warnings: Laboratory faculty perspectives on student barriers for progression to experiential education

IntroductionThis study characterized faculty perceptions of student barriers to achieving an Entrustable Professional Activities (EPA) level 2 or higher in the Patient Care Provider domain. MethodsPharmacy skills laboratory faculty participated in a nominal group technique (NGT) session. Participants reflected on two questions: “What behaviors would result in a student not achieving a rank of EPA readiness level 2 or higher?” and “What knowledge and skills would result in a student not achieving a rank of EPA readiness level 2 or higher?” Participants developed a ranked list using silent brainstorming, idea generation, clarification, and discussion. ResultsTwo NGT sessions were conducted. Group 1 reported (lack of) professionalism, (inability to perform) physical skills, (lack of) critical thinking and interpreting data gathered during physical skills, and (inability to achieve) programmatic outcomes and mile makers exams as barriers. Group 2 ranked behaviors as lack of independence, not taking roles and responsibilities seriously, inability to follow instructions, lack of classroom engagement, and disorganized and unable to prioritize. Group 2 ranked knowledge and skills of significant errors when making medication recommendations, inability to identify accurate medication history, inability to perform tasks with time constraints, poor patient communication, and inability to identify resources. ConclusionsPharmacy skills laboratory faculty can identify behaviors, knowledge, or skills that may prevent a student from achieving an EPA readiness level 2 or higher such as lack of professionalism and poor critical thinking skills and should be empowered to identify early warning signs for students' success and progression to experiential education.

Reworking Emergency Medicine Resident Education Post-Dobbs v Jackson Women's Health Organization.

Reworking Emergency Medicine Resident Education Post-Dobbs v Jackson Women's Health Organization.

World Rank List of Female Judokas and its Relation to Results of Tokyo Olympics (2020)

World Rank List of Female Judokas and its Relation to Results of Tokyo Olympics (2020)

어휘 등급의 검증 방안 연구

In this study, a new method was designed and verified to verify the validity of the 'vocabulary grading list', which selected and graded educational vocabulary to support systematic vocabulary teaching and learning in school education. As a verification method, the following two-way method was devised. First, as a way to expand language life beyond school education, it is a way to verify validity by checking whether the vocabulary grade to be verified is a level that appropriately reflects general language life in the wider world than school education. To implement this plan, a hierarchy of vocabulary extracted from a large-scale corpus, which is a new basic data, was set up and a method of comparative analysis with the grade of this study was designed. Second, it is a convergent method to verify the validity by focusing more on the educational purpose of the vocabulary grade and checking whether it is a level that can explain specific teaching-learning aspects of school education. To implement this plan, a method was designed to reveal the degree of linkage with textbook text readability levels of various subjects through correlation analysis and regression analysis. As a result of the verification, the vocabulary grade of this study was quite consistent with the lexical hierarchy extracted based on the large-scale corpus and the level of readability evaluation of textbook texts, but showed a tendency to show a high degree of concordance, especially in low-level easy vocabulary, and the verification result index was poor. If not, they showed mutual similarity. Through this, it was confirmed that the verification method of this study was partially effective, and it could be said that it was a useful verification result in that it consistently informs the direction of supplementation of the vocabulary ranking list. The vocabulary grade verification method explored in this study is meaningful in that it improved the empirical quality of vocabulary grade setting and grade verification.

Stock ranking prediction using a graph aggregation network based on stock price and stock relationship information

The volatility of stock prices makes it difficult to predict stock price trends correctly. This volatility is affected by many factors, including other stocks related to it. Stock prediction based on graph learning uses various graph neural networks to learn how stocks interact to provide more information. However, they tend to adopt statically defined stock relations based on prior knowledge (such as industry relations and Wiki relations), making it difficult to capture the interplay between stocks over time. In addition, their predictions mostly rely on a single stock relationship, while many types of stock relationships affect the volatility of stock prices in a complex and intertwined manner. A new price similarity relation graph is first constructed using the multi-view stock price similarity to capture dynamic stock relationships. Based on three stock graphs (price similarity, Wiki and industry), we further propose a multi-relational graph attention ranking (MGAR) network. In MGAR, the multi-graph aggregation is achieved by applying adaptive learning mechanisms, thereby forming effective relation embeddings. When combined with the captured price trend embedding, MGAR model gives a ranking list of future returns and chooses K stocks with the best returns to trade so that the return on investment is maximized. Extensive experiments demonstrate that MGAR method outperforms state-of-the-art stock predicting solutions, achieving average returns of 164% and 236% on two real datasets, respectively.

Sport management journals should be rated higher in journal ranking lists! Towards a better international recognition of the field

ABSTRACT Rationale/purpose This article aims to evidence (1) that sport management journals are undervalued in journal ranking lists compared to journals from other fields and (2) how they should be valued, based on an analysis of citation impact indicators (CIIs) (n = 326 journals). Design/methodology/approach For aim 1, regressions are conducted. For aim 2, a “star system” comparable to the Academic Journal Guide (AJG) by the UK Association of Business Schools is applied to CIIs. The boundaries for the CIIs are selected so that the number of AJG 2021 journals in the different star categories aligns with the actual AJG. Findings Regressions show that sport management is undervalued compared to other fields. The application of the star system reveals that, out of 17 sport management journals, the rating of 11 of them based on CIIs is higher than in AJG, while the other six journals perform like in AJG. Practical implications The research can contribute to a better international recognition of sport management journals within the academic community if communicated to journal ranking lists. Research contribution This manuscript helps compare the impact of journals based on an original star system approach applied to CIIs that contributes to establishing meaningful boundaries.

Matching audio/video app features with young user preferences: An integrated approach

AbstractAudio/video mobile applications (apps) enable people to engage in leisure activities without time and space restrictions, which gradually changed human leisure behaviours. This study is one of the first to integrate means‐end chain theory, Kano and conjoint analyses to reveal young user preferences for entertainment audio/video mobile apps. A preliminary qualitative survey with 36 young interviewees established the questionnaire items in the subsequent survey of 389 young respondents. Results show that ‘subtitle choice’, ‘resolution selection’, ‘top ranking list’ and ‘advertising mode and format’ are key attributes that directly influence young user willingness to download apps. In particular, ‘flashing banner ads’, ‘resolution ≤ 480 ppi’, ‘5 seconds to skip ads’, providing ‘subtitle choice’ and without ‘top ranking list’ is the best combination of attribute levels of audio/video apps that meet young users' desires. That is, young users prefer mobile apps with ‘resolution ≤ 480 ppi’ and ‘subtitle’ choices and expect that they can skip flashing banner ads in 5 s. Providing a top audio/video ranking list, however, is unnecessary for young app users. The findings can help marketers and app designers develop effective app designs and advertising strategies for young users to expedite the adoption of entertainment audio/video apps.

Competition for popularity and interventions on a Chinese microblogging site.

Microblogging sites are important vehicles for the users to obtain information and shape public opinion thus they are arenas of continuous competition for popularity. Most popular topics are usually indicated on ranking lists. In this study, we investigate the public attention dynamics through the Hot Search List (HSL) of the Chinese microblog Sina Weibo, where trending hashtags are ranked based on a multi-dimensional search volume index. We characterize the rank dynamics by the time spent by hashtags on the list, the time of the day they appear there, the rank diversity, and by the ranking trajectories. We show how the circadian rhythm affects the popularity of hashtags, and observe categories of their rank trajectories by a machine learning clustering algorithm. By analyzing patterns of ranking dynamics using various measures, we identify anomalies that are likely to result from the platform provider's intervention into the ranking, including the anchoring of hashtags to certain ranks on the HSL. We propose a simple model of ranking that explains the mechanism of this anchoring effect. We found an over-representation of hashtags related to international politics at 3 out of 4 anchoring ranks on the HSL, indicating possible manipulations of public opinion.

Research performance of management scholars in Australian group of eight universities

This article examines research performance of management academics in the Group of Eight (Go8) Australian universities using SCOPUS publication data. Normative research profiles for journal publications, book publications, citations, and h-index are provided for each academic level. The number of journal publications are reported for seven different journal ranking lists. The average Go8 management scholar increases the number of total journal publications per year by 56% over their entire publishing career, but does not increase the number of top international journal publications per year. Importantly, the top quartile of Go8 management scholars – who account for 70% of top journal publications and 79% of journal citations – already achieve world class productivity in the top international journals. We hope Go8 Deans and Heads of School use the research performance benchmarks to inform faculty recruitment, tenure and promotion decisions. JEL Classification: I23, M1, M19

Joint upper & expected value normalization for evaluation of retrieval systems: A case study with Learning-to-Rank methods

While original IR evaluation metrics are normalized in terms of their upper bounds based on an ideal ranked list, a corresponding expected value normalization for them has not yet been studied. We present a framework with both upper and expected value normalization, where the expected value is estimated from a randomized ranking of the corresponding documents present in the evaluation set. We next conducted two case studies by instantiating the new framework for two popular IR evaluation metrics (e.g., nDCG, MAP) and then comparing them against the traditional metrics.Experiments on two Learning-to-Rank (LETOR) benchmark data sets, MSLR-WEB30K (includes 30K queries and 3771K documents) and MQ2007 (includes 1700 queries and 60K documents), with eight LETOR methods (pairwise & listwise), demonstrate the following properties of the new expected value normalized metric: (1) Statistically significant differences (between two methods) in terms of original metric no longer remain statistically significant in terms of Upper Expected(UE) normalized version and vice-versa, especially for uninformative query-sets. (2) When compared against the original metric, our proposed UE normalized metrics demonstrate an average of 23% and 19% increase in terms of Discriminatory Power on MSLR-WEB30K and MQ2007 data sets, respectively. We found similar improvements in terms of consistency as well; for example, UE-normalized MAP decreases the swap rate by 28% while comparing across different data sets and 26% across different query sets within the same data set. These findings suggest that the IR community should consider UE normalization seriously when computing nDCG and MAP and more in-depth study of UE normalization for general IR evaluation is warranted.

Identifying Core Journals in Otolaryngology: A Bibliometric Analysis.

As research in otolaryngology continues to expand rapidly, it is important to identify core journals to keep clinicians updated with the latest advances. This study is the first to characterize core journals in otolaryngology. Using h-index and impact factor (IF), the top 15 NLM-indexed otolaryngology journals were selected for analysis. The references from all articles published in these journals in one randomized quarter were compiled into a citation rank list, with the most cited journal ranked the highest. Citation zonal distribution analysis was conducted to identify the zonal distribution of otolaryngology journals. A total of 3150 journals containing 26876 articles were cited in otolaryngology literature in April-June 2019. Laryngoscope was the most cited journal containing 1762 citations. IF is significantly associated with the h-index for the top 10 otolaryngology journals (p = 0.032). Three core journal zones were identified, with Zone 1 containing 8 journals, Zone 2 containing 36 journals, and Zone 3 containing 189 journals. A linear relationship between the log journal rank for Zones 1-3 and a cumulative number of citations was found (R2 = 0.9948). Eight core journals for otolaryngology were identified: Laryngoscope, Otolaryngology-Head and Neck Surgery, Otology & Neurotology, JAMA Otolaryngology-Head & Neck Surgery, Head & Neck, European Archives of Oto-Rhino-Laryngology, International Journal of Pediatric Otorhinolaryngology, Annals of Otology, Rhinology & Laryngology. In the face of rapidly evolving research and a multitude of journals, the high citation density within these core journals highlights their utility in updating busy clinicians. NA Laryngoscope, 133:3346-3352, 2023.

Neurosurgery residency rankings: An assessment of Doximity’s survey-based data and objective program characteristics

BackgroundMedical students often consult residency program reputation rankings whilst creating residency program rank lists. Accurate assessment of a residency program remains a cumbersome task, primarily due to varying degrees of interpretation and bias. The Doximity Residency Navigator (DRN) is a tool used to assist medical students in making informed residency decisions whilst simultaneously increasing transparency in the residency match process. DRN is comprised of data that assist applicants research prospective training programs, including reputation rankings across specialties, resident and alumni satisfaction surveys, and objective statistics such as training program size and research output. ObjectiveThis study aims to evaluate the correlation between different Doximity rankings across neurosurgical residency programs. MethodsRankings and the relevant criteria (reputation, research output, and percentage of graduates that subspecialize) and program size for all neurological surgery residency programs in the United States were obtained from the 2020–2021 Doximity Residency Navigator. Independent correlation analyses were run to ascertain the relationship between reputation ranking and 1) program size, 2) research output ranking and 3) ranking of the percentage of graduates who subspecialize. ResultsThe reputation rankings of neurosurgical residency programs are determined, in part, by nomination surveys completed by board-certified physicians. A statistically significant correlation was observed between reputation ranking and 1) program size and 2) research output ranking. ConclusionReputation rankings of neurosurgical residency programs put forth by Doximity Inc. incorporate reputational data and other objective outcome criteria. A nuanced understanding of the Doximity rankings will facilitate its use by medical students and healthcare professionals.

Development of a Choice-framework for Covid vaccines in India using a multi-criteria decision analysis approach

BackgroundVaccines were crucial in controlling the Covid-19 pandemic. As more vaccines receive regulatory approval, stakeholders will be faced with several options and must make an appropriate choice for themselves. We proposed a multi-criteria decision analysis (MCDA) framework to guide decision-makers in comparing vaccines for the Indian context. MethodsWe adhered to the ISPOR guidance for the MCDA process. Seven vaccine options were compared under ten criteria. Through three virtual workshops, we obtained opinions and weights from citizens, private-sector hospitals, and public health organisations. Available evidence was rescaled and incorporated into the performance matrix. The final score for each vaccine was calculated for the different groups. We performed different sensitivity analyses to assess the consistency of the rank list. ResultsThe cost, efficacy and operational score of the vaccines had the highest weights among the stakeholders. From the six scenario groups, Janssen had the highest score in four. This was driven by the advantage of having a single dose of vaccination. In the probabilistic sensitivity analysis for the overall group, Covaxin, Janssen, and Sputnik were the first three options. The participants expressed that availability, WHO approvals and safety, among others, would be crucial when considering vaccines. ConclusionsThe MCDA process has not been capitalised on in healthcare decision-making in India and LMICs. Considering the available data and stakeholder preference at the time of the study, Covaxin, Janssen, and Sputnik were preferred options. The choice framework with the dynamic performance matrix is a valuable tool that could be adapted to different population groups and extended based on increasing vaccine options and emerging evidence.*ISPOR – The Professional Society for Health Economics and Outcomes Research.

Comparison of internal medicine applicant and resident characteristics with performance on ACGME milestones

ABSTRACT Internal medicine (IM) residency programs select applicants based on several metrics. Factors predicting success during residency are unclear across studies. To identify whether specific applicant or resident factors are associated with IM resident performance using ACGME milestones. We tested for associations between applicant factors available prior to the start of IM residency and resident factors measured during IM residency training, and resident performance on ACGME milestones across three consecutive years of IM training between 2015-2020. Univariable and multivariable linear regression modeling was used to test associations. Eighty-nine categorical IM residents that completed 3 consecutive years of training were included. Median age was 28 years (IQR 27-29) and 59.6% were male. Mean ACGME milestone scores increased with each post-graduate year (PGY) from 3.36 (SD 0.19) for PGY-1, to 3.80 (SD 0.15) for PGY-2, to 4.14 (SD 0.15) for PGY-3. Univariable modeling suggested referral to the clinical competency committee (CCC) for professionalism concerns was negatively associated with resident performance during each PGY. No applicant or resident factors included in the final multivariable regression models (age at starting residency, USMLE Step scores, interview score, rank list position, ITE scores) were associated with ACGME milestone scores for PGY-1 and PGY-2. Referral to the CCC for professionalism was negatively associated with resident performance during PGY-3. Residency selection factors did not predict resident milestone evaluation scores. Referral to the CCC was associated with significantly worse resident evaluation scores, suggesting professionalism may correlate with clinical performance.

Beyond kemeny rank aggregation: A parameterizable-penalty framework for robust ranking aggregation with ties

Rank Aggregation has ubiquitous applications in operations research, artificial intelligence, computational social choice, and various other fields. Interest in this problem has increased due in part to the need to consolidate lists of rankings and scores output by different decision-making processes and algorithms. Although most attention has focused on the variant of this problem induced by the Kemeny-Snell distance, other robust rank aggregation problems have been proposed. This work delves into the rank aggregation problem under the generalized Kendall-tau distance —a parameterizable-penalty distance measure for comparing rankings with ties— which contains Kemeny aggregation as a special case. First, it derives exact and heuristic solution methods. Second, it introduces a social choice property (GXCC) that encloses existing variations of the Condorcet criterion as special cases, thereby expanding this seminal social choice concept beyond Kemeny aggregation for the first time. GXCC offers both computational and theoretical advantages. In particular, GXCC may help to divide the original problem into smaller subproblems, while still ensuring that solving them independently yields the optimal solution to the original problem. Experiments on two benchmark datasets conducted herein show that the featured exact and heuristic solution methods can benefit from GXCC. Finally, this work derives new theoretical insights into the effects of the generalized Kendall-tau distance penalty parameter on the optimal ranking and on the proposed social choice property.

A Multi-Criteria Analysis of University Housing Options using Weighted Sum Model

This study presents a novel application of the Multi-Criteria Decision-Making (MCDM) Weighted Sum Model (WSM) technique to assist students in selecting suitable university housing options. We identified five key criteria—location, affordability, amenities, room type, and social environment—based on a survey of prospective students' preferences. Using the Analytic Hierarchy Process (AHP), we calculated the weights for each criterion, reflecting the priorities of the surveyed students. We then applied the MCDM-WSM methodology to rank the housing options by calculating the weighted scores for each criterion. Our findings reveal a ranked list of university housing options that can guide students in making well-informed decisions based on their needs and preferences. The study demonstrates the significance of using advanced decision-making techniques in transforming complex decision-making processes into more structured, transparent, and reliable approaches. Furthermore, it highlights potential research gaps and future research directions to enhance the quality of choices and cater to the diverse needs of students in selecting university housing.

Pressured cytotoxic T cell epitope strength among SARS-CoV-2 variants correlates with COVID-19 severity.

Heterogeneity in susceptibility among individuals to COVID-19 has been evident through the pandemic worldwide. Cytotoxic T lymphocyte (CTL) responses generated against pathogens in certain individuals are known to impose selection pressure on the pathogen, thus driving emergence of new variants. In this study, we probe the role played by host genetic heterogeneity in terms of HLA-genotypes in determining differential COVID-19 severity in patients. We use bioinformatic tools for CTL epitope prediction to identify epitopes under immune pressure. Using HLA-genotype data of COVID-19 patients from a local cohort, we observe that the recognition of pressured epitopes from the parent strain Wuhan-Hu-1 correlates with COVID-19 severity. We also identify and rank list HLA-alleles and epitopes that offer protectivity against severe disease in infected individuals. Finally, we shortlist a set of 6 pressured and protective epitopes that represent regions in the viral proteome that are under high immune pressure across SARS-CoV-2 variants. Identification of such epitopes, defined by the distribution of HLA-genotypes among members of a population, could potentially aid in prediction of indigenous variants of SARS-CoV-2 and other pathogens.

Key Competencies of Pediatric Disaster Medicine as Determined by a Systematic Review of Gray Literature

Introduction:Children are often disproportionately impacted by disasters, and yet pediatric specific considerations are not properly emphasized during disaster planning and training, resulting in the desperate needs of children falling through the cracks during disasters. Children differ from adults developmentally, physiologically, and psychologically, and are more vulnerable to negative long-term medical, social, and behavioral outcomes. Additionally, children lack autonomy and rely on adults to gain access to the healthcare system and other resources. Despite the distinctions between adults and children, time and curricula for pediatric disaster training is insufficient, and workforce capacity and competency to plan for and respond to the disaster related needs of children are inadequate; this is especially true for both physicians and other healthcare responders who do not complete a specific pediatric residency. Our study seeks to determine the key core competencies of pediatric disaster medicine that should be included in the training of responders.Method:A systematic gray literature review of existing pediatric disaster medicine curricula was performed, from which a list of the most commonly present key core competencies was created.Results:Data collection and analysis is expected to be completed by April 2023 and will yield a ranked list of core competencies.Conclusion:There is a need for improved pediatric disaster training that addresses the specific considerations of children; this is especially true for non-pediatricians who may be treating children following a disaster. The gray literature review will identify key components of pediatric disaster medicine, which should be applied to all such training curricula to ensure that the care of children who suffer during and after disasters is equitable across the globe.

Applicant and Program Director Perceptions Regarding Virtual Interviewing and Signaling [ID: 1373344

INTRODUCTION: In 2020, obstetrics and gynecology residency programs switched to a virtual interview format because of the COVID-19 pandemic. Most programs have continued to conduct virtual interviews. In 2022, program signaling was introduced to allow candidates applying to obstetrics and gynecology residencies to indicate interest in specific programs. The objective of this study is to survey medical student residency applicants and program directors about their perceptions of the process. METHODS: Education for All (EFA) developed a validated survey assessing student and residency program director perceptions. This survey was distributed online to all residency applicants and program directors who registered for the 2022 EFA Residency Fair. RESULTS: Five hundred six obstetrics and gynecology residency applicants and 33 program directors responded. 37% applicants indicated that virtual interviews would probably provide enough information to create a rank list, but 36% were not sure. 37% of students and 50% of residency programs indicated the process should continue to be virtual. Ninety-one percent of students indicated they would “definitely” use signaling, and 66% of program directors said signaling “might or might not” improve the application process. In a free-text section, many applicants expressed concern with the virtual interview process. CONCLUSION: Fifty percent of residency programs and the majority of residency applicants are unsure if interviews should continue to be virtual. Nearly all candidates planned to use program signaling, but the majority of program directors were unsure if this would improve the application process. Further study is necessary to determine whether program signaling and virtual interviews should be continued in the future.