Higher Star Ratings Research Articles

The impact of green buildings on CO2 emissions: Evidence from commercial and residential buildings

It is essential to evaluate the impact of green buildings on carbon dioxide (CO2) emissions. This study uses a spatial econometric model to analyze the impact of green commercial and residential buildings on CO2 emissions, resulting in the following conclusions: (1) Green commercial buildings can effectively reduce CO2 emissions, while green residential buildings lead to an increase in emissions, indicating a rebound effect. Green residential buildings with higher star ratings (two and three stars) exhibit a significant CO2 reduction effect. (2) As the scale of green commercial buildings grows, the CO2 reduction effect becomes more significant. Additionally, as the scale of green residential buildings increases, the rebound effect of CO2 emissions is not significant. (3) Low-carbon technology and income level can significantly enhance the CO2 reduction effect of green commercial buildings while mitigating the rebound effect of CO2 emissions in green residential buildings.

Medicare Advantage Plan Star Ratings and County Social Vulnerability.

The star rating of a Medicare Advantage (MA) plan is meant to represent plan performance, and it determines the size of quality bonuses. Consumer access to MA plans with a high star rating may vary by the extent of social vulnerability in geographic regions. To examine the association between a county's Social Vulnerability Index (SVI) and the star rating of a county's MA plans. This cross-sectional study used 2023 Centers for Medicare & Medicaid Services data for all MA plans linked to 2020 county-level SVI data from the Centers for Disease Control and Prevention. Data were analyzed from March to October 2023. Quintile rank of county based on composite and theme-specific SVI scores, with quartile 1 (Q1) representing the least vulnerable counties and Q5, the most vulnerable counties. The SVI is a multidimensional measure of a county's social vulnerability across 4 themes: socioeconomic status, household characteristics (such as disability, age, and language), racial and ethnic minority status, and housing type and transportation. County-level mean star rating and the number of MA plans with low-rated (<3.5 stars), high-rated (3.5 or 4.0 stars), and highest-rated (≥4.5 stars) plans. Across 3075 counties, the median county-level star rating was 4.1 (IQR, 3.9-4.3) in Q1 counties and 3.8 (IQR, 3.6-4.0) in Q5 counties (P < .001). The mean star rating of MA plans was lower (difference, -0.24 points; 95% CI, -0.28 to -0.21 points; P < .001), the number of low-rated plans was higher (incidence rate ratio, 1.81; 95% CI, 1.61-2.06; P < .001), and the number of highest-rated plans was lower (incidence rate ratio, 0.75; 95% CI, 0.70-0.81; P < .001) in Q5 counties compared with Q1 counties. Similar patterns were found across theme-specific SVI score quintiles and for 2022 star ratings. In this cross-sectional study, the most socially vulnerable counties were found to have the fewest highest-rated plans for MA beneficiaries. As MA enrollment grows in socially vulnerable regions, this may exacerbate regional differences in health outcomes for Medicare beneficiaries.

Consistency and Sustainability of Home Health Agency Patient Experience Star Rating Performance and the Association With Joint Commission Accreditation

Accreditation seeks to improve the quality of health care, 1 dimension of which is patient experience. The Home Health Consumer Assessment of Healthcare Practitioners and Systems (HHCAHPS) survey collects patient care experience data in home health agencies (HHAs). This study examined the association between accreditation and the consistency and sustainability of high patient experience ratings between Joint Commission (TJC)-accredited and non-TJC accredited HHAs. This multi-year observational study analyzed HHCAHPS star rating performance data from 2015 to 2019. The 5 measures included are based on the 3 publicly reported composite measures, the global measure, and a Star Summary measure. Each measure receives a star rating from 1 to 5. A total of 7230 HHAs reported star rating data between 2015 and 2019, with 4099 (56.7%) having data spanning all 5 years. Of these, 959 (23.4%) were TJC-accredited and 3140 (76.6%) were not. A star rating score of 4 or 5 was considered high performing. TJC-accredited (vs non-TJC accredited) organizations had a higher proportion of HHAs with all 5 years with a high rating ( p &lt; .05 for all measures). Among those HHAs who were in the top rating category the previous year, TJC-accredited (vs non-TJC accredited) HHAs had an even higher probability of sustaining the top rating category in the current year ( p &lt; .001 for all measures). These findings suggest that TJC accreditation is associated with consistently high HHCAHPS star ratings over time. This association was strongest for the Specific Care Issues measure where the concepts addressed by the measure overlapped with topics addressed by accreditation standards.

Analyzing the Evolution of Nepal's Tourism in 2022: Insights and Future Perspectives

This paper focuses into Nepal's internal aviation and tourism industries, underlining the essentiality of strategic planning. It investigates how Nepal's tourism sector has adapted to the global repercussions of COVID-19 and identifies successful strategies to increase tourism revenue following the pandemic. Through the application of both descriptive and inferential statistics, this research analyses data and visually conveys its conclusions. It emphasizes the importance of tailor-made marketing and promotional approaches designed for particular market segments and to exploit local strengths. The document evaluates recovery tactics post-COVID-19, the role of domestic tourism, and the need for upgrades in infrastructure and policies to foster the sustainable development of tourism in Nepal. The necessity for a cohesive approach among federal, provincial, and local governments to facilitate the proper allocation of resources and the execution of tourism initiatives is strongly advocated. The expansion of tourism-related infrastructure, including hotels with high star ratings and improved transport systems, is highlighted as a fundamental factor in supporting the surge of tourists to Nepal. The paper postulates that reinforcing economic development through policies favorable to the tourism industry will have a beneficial impact on international tourism. It urges a policy shift towards the improvement of tourism infrastructure and the adoption of technological innovations in its management.

Factors that affect consumer trust in product quality: a focus on online reviews and shopping platforms

AbstractThe growing popularity of online shopping means that consumers must determine product quality after they make a purchase decision and receive the product, a situation that is directly related to the issue of consumers’ trust toward retailers. This study analyzes the marginal willingness to pay for attributes that influence consumer trust and purchasing decisions regarding products whose quality can only be determined after they are purchased. We select six attributes that influence trust in online shopping: (1) price, (2) number of reviews, (3) “star” rating, (4) review type (i.e., text, picture, and video), (5) length of text reviews, and (6) shopping platform. We conduct a conjoint survey by categorizing brands as famous and nonfamous and analyze the survey data using a multinominal logit model. The results reveal that consumers prefer high star ratings, a large number of reviews, and a trustworthy shopping platform, even if other similar products are less expensive. Additionally, feeling confident about a product’s quality is more difficult for consumers in the case of nonfamous brands, which they have not experienced, compared with famous brands. The findings indicate that when consumers purchase products from nonfamous brands they are willing to pay more for all six attributes. These results can help retailers establish pricing strategies based on the value of trust by considering customer experience.

Ease of Use ratings and real-world child restraint system errors from Safe Kids Illinois seat checks, 2015–2019

Background Child restraint systems (CRSs) significantly reduce risk of crash-related injury, however installation and use errors undermine their benefits. The National Highway Traffic Safety Administration (NHTSA) created the Ease of Use (EOU) rating system to help guide consumers and incentivize manufacturers to improve their products. The EOU rating system assigns one to five stars to four CRS features and overall. Our study assessed the relationship between EOU ratings and CRS installation and use errors documented in seat checks conducted by child passenger safety technicians (CPSTs). Methods We performed a secondary analysis of data from Safe Kids Illinois seat check records from 2015 through 2019 and EOU ratings from 2008 to 2020. Five types of errors were documented by CPSTs. Study authors (JYL and MLM) used a tiered system to match seat check model numbers to EOU ratings. We calculated chi-square statistics and performed logistic regression analyses to assess for EOU as a predictor of relevant CRS errors (e.g., tether errors for forward-facing CRSs). Results Our analyses included 2132 seat check observations, of which 217 (10.2%) were exact, 244 (10.5%) were probable, and 1671 (78.4%) were near matches via sorting and web search. Errors were most common for seat belts (70.7%) and least common for recline angle (36.9%). Star ratings for instructions, vehicle installation, and labels were associated with recline angle and seat belt errors. Star ratings for instructions, labels, and securing child were associated with harness errors. CRSs with 4-star and 5-star ratings had lower odds of errors for recline angle (Odds Ratio (OR) 0.62; 95% Confidence Interval (CI): 0.43, 0.89 and OR 0.31; 95% CI: 0.17, 0.56) lower anchors (OR 0.59; 95% CI 0.40, 0.89 and OR 0.38; 95% CI: 0.21, 0.68), and harness (OR 0.56; 95% CI: 0.40, 0.76 and OR 0.19; 95% CI: 0.10, 0.35) when compared with 1 and 2-star CRSs. Conclusions This study provides evidence in support of NHTSA’s EOU ratings as predictors of some CRS installation and use errors among caregivers who obtain seat checks. A higher star rating may be helpful for caregivers when choosing a CRS that will yield lower installation errors.

How do hotel star ratings affect the relationship between environmental CSR and green word‐of‐mouth?

AbstractHotels have increasingly engaged in environmentally responsible initiatives to demonstrate their commitment to environmental concerns and sustainable hospitality and tourism. These initiatives are expected to become even more popular in the context that the COVID‐19 crisis has driven people to further acknowledge the importance of the ecosystem. This study aims to examine how hotels' environmental corporate social responsibility (CSR) affects customers' green word‐of‐mouth (WOM). Structural equation modeling was employed to analyze data from an online survey of 749 Chinese respondents. The findings reveal that hotels' environmental CSR indirectly enhances customers' green WOM intention via green perceived value (i.e., cognitive route) and green hotel pride (i.e., emotional route). Furthermore, the indirect effects of hotels' environmental CSR on customers' green WOM are more substantial for hotels with higher star ratings. These findings offer valuable insights for hoteliers to develop genuine environmentally responsible initiatives that can generate positive customer responses.

A New Multi-Attribute Decision Making Method for Overvalued Star Ratings Adjustment and Its Application in New Energy Vehicle Selection

Under the global consensus of carbon peaking and carbon neutrality, new energy vehicles have gradually become mainstream, driven by the dual crises regarding the atmospheric environment and energy security. When choosing new energy vehicles, consumers prefer to browse the post-purchase reviews and star ratings of various new energy vehicles on platforms. However, it is easy for consumers to become lost in the high-star text reviews and mismatched reviews. To solve the above two issues, this study selected nine new energy vehicles and used a multi-attribute decision making method to rank the vehicles. We first designed adjustment rules based on star ratings and text reviews to cope with the issue of high star ratings but negative text reviews. Secondly, we classified consumers and recommended the optimal alternative for each type of consumer to deal with the issue of mismatched demands between review writers and viewers. Finally, this study compared the ranking results with the sales charts of the past year to verify the feasibility of the proposed method initially. The feasibility and stability of the proposed method were further verified through comparative and sensitivity analyses.

Correlation Between GitHub Stars and Code Vulnerabilities

In the software industry, open-source repositories are widely utilized to speed up software development. GitHub is a big source of open-source repositories and offers users to star the code repository. Stars are used in GitHub to represent appreciation and popularity. Studies have revealed that repositories may be of lower quality and may have vulnerabilities that hackers may exploit. It is not known whether the popularity of the GitHub repositories in terms of stars confirms the security and invulnerability of the program code. This paper analyzed the correlation between stars of GitHub’s code repositories and the vulnerabilities in their code by using static code analyzer. The study examined the vulnerabilities in ten popular C++ source repositories on GitHub and discovered 3487 vulnerabilities in the dataset, which were split into four categories based on severity. There was not a single repository in the dataset that was free of flaws. On the detected vulnerabilities, a Kruskal-Wallis H test reveals a significant difference between the different code repositories of the dataset. The Spearman's rank correlation coefficient test found no correlation between repositories’ stars and the frequency of vulnerabilities, implying that the popularity of code repositories on GitHub in terms of high star ratings does not imply their security integrity. Overall, the findings suggest that code repositories should be thoroughly evaluated before being used in software development. The novelty of this paper resides in the development of new knowledge as well as the study pattern that may be used to other investigations.

Impact of an educational program and decision tool on choice of maternity hospital: the delivery decisions randomized clinical trial

BackgroundReducing cesarean rates is a public health priority. To help pregnant people select hospitals with lower cesarean rates, numerous organizations publish publically hospital cesarean rate data. Few pregnant people use these data when deciding where to deliver. We sought to determine whether making cesarean rate data more accessible and understandable increases the likelihood of pregnant people selecting low-cesarean rate hospitals.MethodsWe conducted a 1:1 randomized controlled trial in 2019–2021 among users of a fertility and pregnancy mobile application. Eligible participants were trying to conceive for fewer than five months or were 28–104 days into their pregnancies. Of 189,456 participants approached and enrolled, 120,621 participants met entry criteria and were included in analyses. The intervention group was offered an educational program explaining the importance of hospital cesarean rates and an interactive tool presenting hospital cesarean rates as 1-to-5-star ratings. Control group users were offered an educational program about hospital choice and a hospital choice tool without cesarean rate data. The primary outcome was the star rating of the hospital selected by each patient during pregnancy. Secondary outcomes were the importance of cesarean rates in choosing a hospital and delivery method (post-hoc secondary outcome).ResultsOf 120,621 participants (mean [SD] age, 27.8 [7.9]), 12,284 (10.2%) reported their choice of hospital during pregnancy, with similar reporting rates in the intervention and control groups. Intervention group participants selected hospitals with higher star ratings (2.52 vs 2.16; difference, 0.37 [95% CI, 0.32 to 0.43] p < 0.001) and were more likely to believe that the hospitals they chose would impact their chances of having cesarean deliveries (38.5% vs 33.1%, p < 0.001) but did not assign higher priority to cesarean delivery rates when choosing their hospitals (76.2% vs 74.3%, p = 0.05). There was no difference in self-reported cesarean rates between the intervention and control groups (31.4% vs 31.4%, p = 0.98).ConclusionPeople offered an educational program and interactive tool to compare hospital cesarean rates were more likely to use cesarean data in selecting a hospital and selected hospitals with lower cesarean rates but were not less likely to have a cesarean.Clinical Trial RegistrationRegistered December 9, 2016 at clinicaltrials.gov, First enrollment November 2019. ID NCT02987803, https://clinicaltrials.gov/ct2/show/NCT02987803

Hemodialysis facility star rating affects mortality in chronic hemodialysis patients: a longitudinal observational cohort study.

Many countries have their own hemodialysis (HD) quality assurance programs and star rating systems for HD facilities. However, the effects of HD quality assurance programs on patient mortality are not well understood. Therefore, in the present study, the effects of the Korean HD facility star rating on patient mortality in maintenance HD patients were evaluated. This longitudinal, observational cohort study included 35,271 patients receiving HD treatment from 741 facilities. The fivestar ratings of HD facilities were determined based on HD quality assessment data from 2015, which includes 12 quality measures in structural, procedural, and outcome domains. The patients were grouped into high (three to five stars) and low (one or two stars) groups based on HD facility star rating. Cox proportional hazards model was used to evaluate the effects of star rating on patient mortality during the mean follow-up duration of 3 years. The patient ratio between high and low HD facility star rating groups was 82.0% vs. 18.0%. The patients in the low star rating group showed lower single-pool Kt/V and higher calcium and phosphorus levels compared with subjects in the high star rating group. After adjusting for sociodemographic and clinical parameters, the HD facility star rating independently increased the mortality risk (hazard ratio, 1.11; 95% confidence interval, 1.04-1.18; p = 0.002). The HD facilities with low star rating showed higher patient mortality.

Sentiment Analysis of Online Patient-Written Reviews of Vascular Surgeons

Online patient reviews influence a patient's choice of a vascular surgeon. The aim of this study is to examine underlying factors that contribute to positive and negative patient reviews by leveraging sentiment analysis and machine learning methods. The Society of Vascular Surgeons publicly accessible member directory was queried and cross-referenced with a popular patient-maintained physician review website, healthgrades.com. Sentiment analysis and machine learning methods were used to analyze several parameters. Demographics (gender, age, and state of practice), star rating (of 5 stars), and written reviews were obtained for corresponding vascular surgeons. A sentiment analysis model was applied to patient-written reviews and validated against the star ratings. Student's t-test or one-way analysis of variance assessed demographic relationships with reviews. Word frequency assessments and multivariable logistic regression analyses were conducted to identify common and determinative components of written reviews. A total of 1,799 vascular surgeons had public profiles with reviews. Female gender of surgeon was associated with lower star ratings (male=4.19, female=3.95, P<0.01) and average sentiment score (male=0.50, female=0.40, P<0.01). Younger physician age was associated with higher star rating (P=0.02) but not average sentiment score (P=0.12). In the Best reviews, the most commonly used one-words were Care (N=999), Caring (N=767), and Kind (N=479), while the most commonly used two-word pairs were Saved/Life (N=189), Feel/Comfortable (N=106), and Kind/Caring (N=104). For the Worst reviews, the most commonly used one-words were Pain (N=254) and Rude (N=148), while the most commonly used two-word pairs were No/One (N=27), Waste/Time (N=25), and Severe/Pain (N=18). In a multiple logistic regression, satisfactory reviews were associated with words such as Confident (odds ratio [OR]=8.93), Pain-free (OR=4.72), Listens (OR=2.55), and Bedside Manner (OR=1.70), while unsatisfactory reviews were associated with words such as Rude (OR=0.01), Arrogant (OR=0.09), Infection (OR=0.20), and Wait (OR=0.48). Female surgeons received significantly worse reviews and younger surgeons tended to receive better reviews. The positivity and negativity of reviews were largely related to words associated with the patient-doctor experience and pain. Vascular surgeons should focus on these 2 areas to improve patient experiences and their own reviews.

Single-Session Interventions Embedded Within Tumblr: Acceptability, Feasibility, and Utility Study.

BackgroundExisting mental health treatments are insufficient for addressing mental health needs at scale, particularly for teenagers, who now seek mental health information and support on the web. Single-session interventions (SSIs) may be particularly well suited for dissemination as embedded web-based support options that are easily accessible on popular social platforms.ObjectiveWe aimed to evaluate the acceptability and effectiveness of three SSIs, each with a duration of 5 to 8 minutes (Project Action Brings Change, Project Stop Adolescent Violence Everywhere, and REFRAME)—embedded as Koko minicourses on Tumblr—to improve three key mental health outcomes: hopelessness, self-hate, and the desire to stop self-harm behavior.MethodsWe used quantitative data (ie, star ratings and SSI completion rates) to evaluate acceptability and short-term utility of all 3 SSIs. Paired 2-tailed t tests were used to assess changes in hopelessness, self-hate, and the desire to stop future self-harm from before to after the SSI. Where demographic information was available, the analyses were restricted to teenagers (13-19 years). Examples of positive and negative qualitative user feedback (ie, written text responses) were provided for each program.ResultsThe SSIs were completed 6179 times between March 2021 and February 2022. All 3 SSIs generated high star ratings (>4 out of 5 stars), with high completion rates (approximately 25%-57%) relative to real-world completion rates among other digital self-help interventions. Paired 2-tailed t tests detected significant pre-post reductions in hopelessness for those who completed Project Action Brings Change (P<.001, Cohen dz=−0.81, 95% CI −0.85 to −0.77) and REFRAME (P<.001, Cohen dz=−0.88, 95% CI −0.96 to −0.80). Self-hate significantly decreased (P<.001, Cohen dz=−0.67, 95% CI −0.74 to −0.60), and the desire to stop self-harm significantly increased (P<.001, Cohen dz=0.40, 95% CI 0.33 to 0.47]) from before to after the completion of Project Stop Adolescent Violence Everywhere. The results remained consistent across sensitivity analyses and after correcting for multiple tests. Examples of positive and negative qualitative user feedback point toward future directions for SSI research.ConclusionsVery brief SSIs, when embedded within popular social platforms, are one promising and acceptable method for providing free, scalable, and potentially helpful mental health support on the web. Considering the unique barriers to mental health treatment access that many teenagers face, this approach may be especially useful for teenagers without access to other mental health supports.

Relationship between Vehicle Safety Ratings and Drivers' Injury Severity in the Context of Gender Disparity.

Previous studies have analyzed the relationship between vehicle safety ratings from impact tests and actual crash injury severity. Nevertheless, no study has investigated the relationship in the context of gender disparity. The main objective of this paper is to explore the validity of the 5-star ratings of the U.S. National Highway Traffic Safety Administration, which describes vehicles’ protectiveness, using actual traffic crash data by gender. Random parameter models are developed using 2015–2020 two-vehicle crash data from Maryland, United States. According to the data, over 90% of vehicles have 4–5 stars in overall, front-impact, and side-impact 5-star ratings. After controlling other factors, it is shown that woman drivers are more likely to be seriously injured in two-vehicle crashes than men drivers when using vehicles with the same 5-star safety ratings. Moreover, there is significant individual heterogeneity in the effect of vehicles with different 5-star safety ratings on driver injury severity. Using vehicles with more stars can reduce the risk of being seriously injured for most man drivers. However, the probability of woman drivers being seriously injured is reduced by approximately 5% on average by using vehicles with higher star ratings in the overall and front-impact 5-star rating, and individual heterogeneity shows a difference of nearly 50% in positive and negative effects. The overall and front-impact 5-star ratings of vehicles could not provide reasonable information as the safety performance of vehicles in traffic crashes for woman drivers. On the other hand, drivers’ residence, driving characteristics, crash types, and environmental characteristics are significantly associated with the injury severity. It is expected that the results from this study will contribute to guide a better vehicle safety design for both men and women.

Limiting managerial discretion by regulation: nursing homes and the national background check program

Structuring managerial discretion has been a key government policy tool in contemporary regulation and governance. This article explores how a policy that constrains managers’ discretion in recruitment influences the performance of public services. The National Background Check Program (NBCP) is a federal program aimed at strengthening states’ criminal background checks targeting direct patient access employees in nursing homes, where abuse, neglect and misappropriations have been a persistent concern. We combine secondary administrative panel data on Medicare and Medicaid certified nursing facilities with primary data collected from states on their NBCP efforts. We find that NBCP participation, funding and the implementation of fingerprinting requirement, in particular, are associated with fewer deficiencies and higher star ratings. These findings suggest that, while constraining managerial discretion, government regulation is an important tool that federal and state agencies can use to control the performance of public and private entities in some markets and provide market enhancing signals to consumers.

Orthodontics on sale: fixed appliances on E-commerce platforms

Abstract Introduction The sale of orthodontic materials, considered as medical devices, is regulated in order to protect patients. The availability of orthodontic products on e-commerce platforms, however, may go unregulated and compromise patient safety. Objective The present study aimed to investigate the sale of fixed orthodontic appliances on a local and global e-commerce platform. Materials and Methods A cross-sectional analysis of orthodontic materials sold on Shopee and Amazon was conducted using a specific keyword search for brackets, arch wires and ligatures. After deleting duplicates and irrelevant search results, the top 50 products for each keyword search were analysed. Product name, the type of product, seller, cost, star rating, the number of reviews, the number of sales, shipping location and brand identification were recorded. Brand registration with a medical device regulatory authority was checked. Collected data were tabulated in Google (spread)sheet to derive descriptive statistics and frequency tables. Results A total of 800 search results were screened. Three hundred and forty-eight relevant results were identified, approximately two-thirds of which originated from Shopee. Ligatures generated the highest search outcome in both Shopee (39%) and Amazon (80%). Fixed orthodontic materials sold on Shopee resulted in a larger number of reviews, higher star ratings and a lower cost per item compared to those supplied by Amazon. Products on Amazon were more likely to have brand identification (97%) compared to Shopee (5%). None of the identifiable brands on Shopee were registered with regulatory bodies, whereas eight products listed on Amazon were registered. A large portion of shipping locations originated from China for both e-commerce platforms. Conclusions Orthodontic brackets, arch wires and ligatures are more readily available on Shopee compared to Amazon. The quality and safety of these products cannot be assured as the majority of the products were either not clearly labelled or their registration was not identified under the regulation of medical devices.

A Bird in Hand: An Examination of the Influence of Nursing School Proximal Density on Hospital Quality of Care Outcomes in U.S. Hospitals.

While nurse staffing shortage is generally true, it is not universal, and it remains unclear the degree to which variation in local staffing markets might influence the relationship between nurse staffing and care quality. This study seeks to determine the effect of nurse staffing markets on the quality of hospital care delivered in U.S. hospitals by examining the relationship between the proximal density of nurse staffing resources to hospitals and patient-reported care quality outcomes. This examination analyzes hospital performance on (Hospital Consumer Assessment of Healthcare Providers and Systems) HCAHPS based on the proximal density of nursing schools. The analysis combines data from Centers for Medicare and Medicaid Services (CMS) Hospital Compare (N1 = 2959) and U.S. nursing school locations from the American Association of Colleges of Nursing (N2 = 811) via a series of binary logistic regressions to determine whether local nurse staffing availability is related to hospital’s attainment of either low or high star quality ratings. A sensitivity analysis is also offered to determine the association with 1, 3, and 5-star ratings. The findings suggest that the odds of receiving both a low-star rating and a high-star rating of HCAHPS performance increase as proximal density increases while the odds of receiving a 3-star rating decrease. Hospitals are able to achieve the highest levels of performance as high performing hospitals in high-density markets seem to be taking advantage of resource availability to establish close, strong ties with nurse staffing resources as opposed to viewing nurses as an easily replaceable resource.

Support Vector Machine: Analisis Sentimen Aplikasi Saham di Google Play Store

The best application predicate has been awarded to the application that has the highest downloads and high star rating on Google Play Store. In rating an application, user comments need to be considered because many stock investment apps have almost the same downloads and star ratings, so the title of best app is a problem. Based on this condition, this research aims to analyze user feedback of stock investment applications as a variable to determine which stock investment application is the best on the Google Play Store. This study using the Support Vector Machine (SVM) classification method with the support of Rapid Miner to carry out the calculation process. From this research, it produces positive sentiment values for each application, namely HSB Investment about 1,134 positive sentiments with an accuracy value of 88.70%, Ajaib about 936 positive sentiments with an accuracy value of 61.89%, Pluang about 703 positive sentiments with an accuracy value of 68.25%, Bibit about 322 positive sentiments with an accuracy value of 64.89%, and Stockbit about 124 positive sentiments with an accuracy value of 66.95%. So it can be concluded that the HSB application as the best stock investment application based on user comments reviews where this application has the most positive sentiment reviews with a high accuracy value.

Understanding the Tradeoffs Between Travel Burden and Quality of Care for In-center Hemodialysis Patients.

Renal dialysis is a lifesaving but demanding therapy, requiring 3 weekly treatments of multiple-hour durations. Though travel times and quality of care vary across facilities, the extent to which patients are willing and able to engage in weighing tradeoffs is not known. Since 2015, Medicare has summarized and reported quality data for dialysis facilities using a star rating system. We estimate choice models to assess the relative roles of travel distance and quality of care in explaining patient choice of facility. Using national data on 2 million patient-years from 7198 dialysis facilities and 4-star rating releases, we estimated travel distance to patients' closest facilities, incremental travel distance to the next closest facility with a higher star rating, and the difference in ratings between these 2 facilities. We fit mixed effects logistic regression models predicting whether patients dialyzed at their closest facilities. Median travel distance was 4 times that in rural (10.9 miles) versus urban areas (2.6 miles). Higher differences in rating [odds ratios (OR): 0.56; 95% confidence interval (CI): 0.50-0.62] and greater area deprivation (OR: 0.50; 95% CI: 0.48-0.53) were associated with lower odds of attending one's closest facility. Stratified models were also fit based on urbanicity. For rural patients, excess travel was associated with higher odds of attending the closer facility (per 10 miles; OR: 1.05; 95% CI: 1.04-1.06). Star rating differences were associated with lower odds of receiving care from the closest facility among urban (OR: 0.57; 95% CI: 0.51-0.63) and rural patients (OR: 0.18; 95% CI: 0.08-0.44). Most dialysis patients have higher rated facilities located not much further than their closest facility, suggesting many patients could evaluate tradeoffs between distance and quality of care in where they receive dialysis. Our results show that such tradeoffs likely occur. Therefore, quality ratings such as the Dialysis Facility Compare (DFC) Star Rating may provide actionable information to patients and caregivers. However, we were not able to assess whether these associations reflect a causal effect of the Star Ratings on patient choice, as the Star Ratings served only as a marker of quality of care.

Reading Between the Stars: Understanding the Effects of Online Customer Reviews on Product Demand

Problem definition: Many studies have examined quantitative customer reviews (i.e., star ratings) and found them to be a reliable source of information that has a positive effect on product demand. Yet the effect of qualitative customer reviews (i.e., text reviews) on demand has been less thoroughly studied, and it is not known whether (or how) the sentiment expressed in text reviews moderates the influence of star ratings on product demand. We are therefore led to examine how the interplay between review sentiment and star ratings affects product demand. Academic/practical relevance: Consumer perceptions of product quality and how they are shared via customer reviews are of extreme relevance to the firm, but we still do not understand how product demand is affected by the quantitative and qualitative aspects of customer reviews. Our paper seeks to fill this critical gap in the literature by analyzing star ratings, the sentiment of customer reviews, and their interaction. Methodology: Using 2002–2013 data for the U.S. automobile market, we investigate empirically the impact of star ratings and review sentiment on product demand. Thus, we estimate an aggregated multinomial choice model after performing a machine learning–based sentiment analysis on the entire corpus of customer reviews included in our sample. We take advantage of a quasi-exogenous shock to establish a causal link between online reviews and product demand. Results: We find robust empirical evidence that (i) review sentiment and star ratings both have a decreasingly positive effect on product demand and (ii) the effect (on demand) of their interaction suggests that the two components of reviews are complements. Positive sentiments in text reviews increase the positive effect of ratings when the effect of ratings is decidedly positive while they also compensate for the tendency of consumers to discount extremely high star ratings. Managerial implications: The firm should pay greater attention to quantitative and qualitative customer reviews to better understand how consumers perceive the quality of its offerings.