Star Rating Research Articles

Statistical estimation of fatal and serious injuries saved by iRAP protocols in 74 countries.

Road traffic crashes cause 1.19 million deaths and millions more injuries annually. The persistently high burden has drawn attention from national and international stakeholders worldwide. Unsafe road infrastructure is one of the major risk factors for traffic safety, particularly in low- and middle-income countries. Aiming to eliminate high-risk roads in all countries, the International Road Assessment Programme (iRAP) developed a robust and evidence-based approach to support country transportation agencies. Thus far, the iRAP protocols have been used to collect 1.8 million kilometers of Crash Risk Mapping and 1.5 million kilometers of Star Rating and FSI estimations in 128 countries. Deploying an observational before-and-after (or pre-post) study design, this report estimated the fatal and series injuries (FSI) saved through use of the iRAP protocols. The study is based on 441,753 kilometers of assessed roads from 1,039 projects in 74 countries. Our results show that the implementation of iRAP's proposed countermeasures saves about 159,936 FSI annually. Throughout the lifetime of the implemented countermeasures, a total of 3.2 million FSI could be saved. While quantifying the success of the iRAP protocols, our results suggest an opportunity to save many millions more lives on the roads through expanding iRAP implementation to more regions and countries.

Trends in HCAHPS Survey Scores, 2008-2019: A Quality Improvement Perspective.

HCAHPS' 2008 initial public reporting, 2012 inclusion in the Hospital Value-Based Purchasing Program (HVBP), and 2015 inclusion in Hospital Star Ratings were intended to improve patient experiences. Characterize pre-COVID-19 (2008-2019) trends in hospital consumer assessment of healthcare providers and systems (HCAHPS) scores. Describe HCAHPS score trends overall, by phase: (1) initial public reporting period (2008-2013), (2) first 2 years of HVBP (2013-2015), and (3) initial HCAHPS Star Ratings reporting (2015-2019); and by hospital characteristics (HCAHPS decile, ownership, size, teaching affiliation, and urban/rural). A total of 3909 HCAHPS-participating US hospitals. HCAHPS summary score (HCAHPS-SS) and 9 measures. The mean 2007-2019 HCAHPS-SS improvement in most-positive-category ("top-box") responses was +5.2 percentage points/pp across all hospitals (where differences of 5pp, 3pp, and 1pp are "large," "medium," and "small"). Improvement rate was largest in phase 1 (+0.8/pp/year vs. +0.2pp/year and +0.1pp/year for phases 2 and 3, respectively). Improvement was largest for Overall Rating of Hospital (+8.5pp), Discharge Information (+7.3pp), and Nurse Communication (+6.5pp), smallest for Doctor Communication (+0.8pp). Some measures improved notably through phases 2 and 3 (Nurse Communication, Staff Responsiveness, Overall Rating of Hospital), but others slowed or reversed in Phase 3 (Communication about Medicines, Quietness). Bottom-decile hospitals improved more than other hospitals for all measures. All HCAHPS measures improved rapidly 2008-2013, especially among low-performing (bottom-decile) hospitals, narrowing the range of performance and improving scores overall. This initial improvement may reflect widespread, general quality improvement (QI) efforts in lower-performing hospitals. Subsequent slower improvement following the introduction of HVBP and Star Ratings may have reflected targeted, resource-intensive QI in higher-performing hospitals.

Claims-Based vs Agency-Reported Patient Outcomes Among Home Health Agencies, 2013-2019

Given the growth of home health agency (HHA) care, it is important to understand whether quality reporting programs, such as star ratings, are associated with improved patient outcomes. To assess the immediate and long-term association of the introduction of HHA star ratings with patient-level quality outcomes, comparing claims-based and agency-reported measures. This cross-sectional study used Medicare HHA claims and agency-reported assessments to identify sequential patient episodes (ie, spells) among US adults with traditional Medicare who received HHA care (2013-2019). An interrupted time series (ITS) model was used to measure changes in trends and levels before and after the introduction of star ratings. Statistical analysis was performed from November 2022 to September 2023. The exposure was the introduction of HHA star ratings. The postexposure period was set as starting January 1, 2016, to account for the period when both star ratings (quality of patient care and patient satisfaction rating) were publicly reported. The main outcomes included claims-based hospitalization measures (both during the patient spell and 30 days after HHA discharge) and agency-reported functional measures, such as improvement in ambulation, bathing, and bed transferring. There was also a measure to capture timely initiation of care among post-acute care HHA users, defined as HHA care initiated within 2 days of inpatient discharge. This study identified 22 958 847 patient spells to compare annual changes over time; 9 750 689 patient spells were included during the pre-star ratings period from January 1, 2013, to December 31, 2015 (6 067 113 [62.2%] female; 1 100 145 [11.3%] Black, 512 487 [5.3%] Hispanic, 7 845 197 [80.5%] White; 2 656 124 [27.2%] dual eligible; mean [SD] patient spell duration, 70.9 [124.9] days; mean [SD] age, 77.4 [12.0] years); 13 208 158 patient spells were included during the post-star ratings period from January 1, 2016, to December 31, 2019 (8 104 69 [61.4%] female; 1 385 180 [10.5%] Black, 675 536 [5.1%] Hispanic, 10 664 239 [80.7%] White; 3 318 113 [25.1%] dual eligible; mean [SD] patient spell duration, 65.3 [96.2] days; mean [SD] age, 77.7 [11.6] years). Results from the ITS models found that the introduction of star ratings was associated with an acceleration in the mean [SE] hospitalization rate during the spell (0.39% [0.05%] per year) alongside functional improvements in ambulation (2.40% [0.29%] per year), bed transferring (3.95% [0.48%] per year) and bathing (2.34% [0.19%] per year) (P < .001). This occurred alongside a 1.21% (0.12%) per year reduction in timely initiation of care (P < .001). This cross-sectional study found an observed improvement in agency-reported functional measures, which contrasted with slower increases in more objective measures such as hospitalization rates and declines in timely initiation of care. These findings suggest a complex picture of HHA quality of care after the introduction of star ratings.

Drivers of COVID-19 Outcomes in Long-Term Care Facilities Using Multi-Level Analysis: A Systematic Review.

This study aimed to identify the individual, organizational, and environmental factors which contributed to COVID-19-related outcomes in long-term care facilities (LTCFs). A systematic review was conducted to summarize and synthesize empirical studies using a multi-level analysis approach to address the identified influential factors. Five databases were searched on 23 May 2023. To be included in the review, studies had to be published in peer-reviewed journals or as grey literature containing relevant statistical data. The Joanna Briggs Institute critical appraisal tool was employed to assess the methodological quality of each article included in this study. Of 2137 citations identified after exclusions, 99 records met the inclusion criteria. The predominant individual, organizational, and environmental factors that were most frequently found associated with the COVID-19 outbreak comprised older age, higher dependency level; lower staffing levels and lower star and subset domain ratings for the facility; and occupancy metrics and co-occurrences of outbreaks in counties and communities where the LTCFs were located, respectively. The primary individual, organizational, and environmental factors frequently linked to COVID-19-related deaths comprised age, and male sex; higher percentages of racial and ethnic minorities in LTCFs, as well as ownership types (including private, for-profit, and chain membership); and higher occupancy metrics and LTCF's size and bed capacity, respectively. Unfolding the risk factors collectively may mitigate the risk of outbreaks and pandemic-related mortality in LTCFs during future endemic and pandemics through developing and improving interventions that address those significant factors.

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.

Decoding Energy Usage Predictions: An Application of XAI Techniques for Enhanced Model Interpretability

The growing complexity of machine learning models has heightened the need for interpretability, particularly in applications impacting resource management and sustainability. This study addresses the challenge of interpreting predictions from sophisticated machine learning models used for building energy consumption predictions. By leveraging Explainable AI (XAI) techniques, including Permutation Importance, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-Agnostic Explanations (LIME), we have dissected the predictive features influencing building energy usage. Our research delves into a dataset consisting of various building characteristics and weather conditions, applying an XGBoost model to predict Site Energy Usage Intensity (Site EUI). The Permutation Importance method elucidated the global significance of features across the dataset, while SHAP provided a dual perspective, revealing both the global importance and local impact of features on individual predictions. Complementing these, LIME offered rapid, locally focused interpretations, showcasing its utility for instances where immediate insights are essential. The findings indicate that 'Energy Star Rating', 'Facility Type', and 'Floor Area' are among the top predictors of energy consumption, with environmental factors also contributing to the models' decisions. The application of XAI techniques yielded a nuanced understanding of the model's behavior, enhancing transparency and fostering trust in the predictions. This study contributes to the field of sustainable energy management by demonstrating the application of XAI for insightful model interpretation, reinforcing the significance of interpretable AI in the development of energy policies and efficiency strategies. Our approach exemplifies the balance between predictive accuracy and the necessity for model transparency, advocating for the continued integration of XAI in AI-driven decision-making processes.

Reverse engineering the last-minute on-line pricing practices: an application to hotels

We suggest a nonlinear time series methodology to model the (last-minute) price adjustments that hotels active in the online market make to adapt their early-booking rates in response to unpredictable fluctuations in demand. We use this approach to reverse-engineer the pricing strategies of six hotels in Milan, Italy, each with different features and services. The results reveal that the hotels’ ability to align last-minute adjustments with early-booking decisions and account for stochastic demand seasonality varies depending on factors such as size, star rating, and brand affiliation. As a primary empirical finding, we show that the autocorrelations of the first four moments of the last-minute price adjustment can be used to gain crucial insights into the hoteliers’ pricing strategies. Scaling up this approach has the potential to equip policymakers in smart destinations with a reliable and transparent tool for the real-time monitoring of demand dynamics.

Sociodemographic differences in nutrition labels effect on Chilean and Mexican youth.

To examine sociodemographic differences in the awareness, understanding, use and effect of nutrition labels among Mexican and Chilean youth. Online surveys among youth (10-17 years) were obtained in 2019 (n=2631). Participants reported their awareness, understanding, and use of their country-specific nutrition facts tables (NFT) and front-of-pack labels (FOPL) (Chile: warning labels [WLs]; Mexico: guideline daily amounts [GDA]). Additionally, participants reported their perceived healthfulness of a sweetened fruit drink after viewing one of six versions of it with different FOPL (no-label control, Health Star Rating, WLs, GDAs, Traffic Light, or Nutri-Score) during an experimental task. Higher self-reported nutrition knowledge was associated with higher NFT and FOPL awareness, understanding, and use, except for WL use. WLs were the most effective FOPL in decreasing the perceived healthfulness of the sweetened fruit drink compared to a no-label condition and other FOP labels. In Chile, the effect of GDA differed by income adequacy, while in Mexico Nutri-Score differed by age. Results suggest that nutrition label awareness, use, understanding, and impact differ across demographics, favoring higher income and nutrition knowledge. Despite this, WLs are likely to have a positive impact on nutrition-related knowledge and behaviors among Mexican and Chilean youth, independently of their socio-demographic groups.

Assessing the environmental impacts of three different types of accommodations in Portugal and Spain by using an LCA approach

The tourism industry, affected by COVID-19, must reduce greenhouse gas emissions. This study evaluated the environmental impact of three hotels in coastal and mountainous regions of Spain and Portugal using Life Cycle Assessment (LCA). Data was gathered via surveys in the Greentour tool. Results indicate that the 2-star hotel (focused on cultural-urban tourism) has the highest impacts in most categories, except for CC, FRD, and POF indicators. The 3-star hotel (beach tourism) contributes the most to CC and FRD indicators, while the hostel (nature-religious tourism) has the highest value in the POF indicator. LCA findings reveal that diesel consumption in the hostel and electricity usage in both the 2-star and 3-star hotels are major contributors to environmental impacts across various categories. Overall, evidence suggests that fossil fuel and electricity usage significantly affect tourism activities environmentally. Interestingly, this study highlights that a 2-star hotel can have a higher carbon footprint (CC indicator) compared to a 3-star hotel, challenging the notion that higher star ratings imply lower environmental impact.

Carbon Emission and Footprints of Hospitality Industries in Akure Metropolis, South Western Nigeria

Climate change and global warming have been identified as major threats to the development of existing ecosystem services and local livelihoods. Mitigating greenhouse gas (GHGs) emissions has become an urgent global policy trajectory with countries acceding to related Treaties and setting national targets. This forms the basis for undertaken this study with a view to assess and report on the Green House Gas (GHG) emission of some hospitality industries in Akure, Nigeria. Using qualitative methodology, the carbon footprint of thirty hotels was assessed. The hotels were selected based on star ratings and categories. Purposive sampling was used to select the respondents based on management heads of the hotels while the result was presented qualitatively using thematic analysis. Results revealed that the sources of GHGs emissions are constituted by human activities including consumption of electricity, fuel combustion, waste generation and laundry services, of which waste and fuel combustion are the highest factor. Reports also showed that the bigger the hotel in term of star rating and category the higher the carbon and GHG emissions. The evaluated hotels emit significant amount of greenhouse gases each year which is detrimental to environment health, The study recommended further research to assess the applicability of the identified strategies for enhancing environmental performance of hospitality industry and to quantify GHGs emissions from cooling appliance within the hospitality industry in the study area.

Supporting companies to reform nutrition policies and practices (REFORM): a multi-centre cluster randomised controlled trial

Unhealthy food environments are major drivers of obesity and diet-related diseases(1). Improving the healthiness of food environments requires a widespread organised response from governments, civil society, and industry(2). However, current actions often rely on voluntary participation by industry, such as opt-in nutrition labelling schemes, school/workplace food guidelines, and food reformulation programmes. The aim of the REFORM study is to determine the effects of the provision of tailored support to companies on their nutrition-related policies and practices, compared to food companies that are not offered the programme (the control). REFORM is a two-country, parallel cluster randomised controlled trial. 150 food companies were randomly assigned (2:1 ratio) to receive either a tailored support intervention programme or no intervention. Randomisation was stratified by country (Australia, New Zealand), industry sector (fast food, other packaged food/beverage companies), and company size. The primary outcome is the nutrient profile (measured using Health Star Rating [HSR]) of foods and drinks produced by participating companies at 24 months post-baseline. Secondary outcomes include company nutrition policies and commitments, the nutrient content (sodium, sugar, saturated fat) of products produced by participating companies, display of HSR labels, and engagement with the intervention. Eighty-three eligible intervention companies were invited to take part in the REFORM programme and 21 (25%) accepted and were enrolled. Over 100 meetings were held with company representatives between September 2021 and December 2022. Resources and tailored reports were developed for 6 touchpoints covering product composition and benchmarking, nutrition labelling, consumer insights, nutrition policies, and incentives for companies to act on nutrition. Detailed information on programme resources and preliminary 12-month findings will be presented at the conference. The REFORM programme will assess if provision of tailored support to companies on their nutrition-related policies and practices incentivises the food industry to improve their nutrition policies and actions.

Assessment of food availability in New Zealand primary schools

Childhood obesity and overweight rates in New Zealand are considerably higher than that globally with one in three children aged between 2-14 years being overweight or obese(1). Children’s dietary knowledge and food preferences are influenced by various factors including the food environment. Schools are an excellent setting to influence children’s dietary behaviours since they have the potential to reach almost all children during the first two decades of their lives. However, previous analyses indicate many school canteens and food providers do not supply foods that promote healthy eating and nutrition behaviours (2,3). The Ministry of Health (MoH) recently implemented a ‘Food and Drink Guidance for Schools’ which utilises a traffic-light framework dividing foods into three categories: ‘green’, ‘amber’, and ‘red’(4). The aim of this study was to assess primary school canteen food menus against the newly implemented MoH Guidance. A convenience sample of 133 primary school canteen menus were collected in 2020 as part of the baseline evaluation of the Healthy Active Learning initiative across New Zealand. Four researchers (three nutritionists and one dietitian) developed a menu analysis toolkit to undertake the analysis of all menus collected. The toolkit provided a breakdown of commonly packaged foods and meals/menu items available to purchase within schools based on Health Star Ratings, ingredients, and/or standard recipes. Assumptions were created for menu items requiring additional detail to be categorised according to the guidance through consensus by all four researchers. Primary school menus were coded by two researchers, and intercoder reliability was ensured by independent coding and cross-checking of 10% of menus. Descriptive and inferential analyses were conducted using IBM SPSS and P&lt;0.05 denoted significance. Analyses of canteen menus revealed that most menu items belonged to the less healthy amber (41.0%) and red (40%) food categories. Low decile schools had a lower percentage of green food items (8.6%) and a higher percentage of red food items (48.3%) compared to high decile schools (p = 0.028). Similarly, schools in low deprivation areas had a significantly higher percentage of green food items (14.2%) compared to high deprivation areas (8.6%) (p = 0.031). Sandwiches, filled rolls, and wraps were the most commonly available items (86%) followed by baked foods and foods with pastry (71%). Sugar-sweetened beverages were just as prevalent as water on school food menus (54% each). Over half of in-house catered canteen menu items were classified as 'red’ foods (55.3%). This study highlights that most school canteens were not meeting the guidelines for healthy food and drink provision outlined by the MoH. Improving school food availability for children in socioeconomically deprived areas needs to be prioritised to reduce inequities. Findings suggest the need for more robust national policies and mandated school guidance to improve the food environments in New Zealand schools.

Practicing the Concept of Ubiquitous Learning: Constructing an Online Music Course on an International Education Platform

We developed a one-year course run of String Music Appreciation as an international online course on the British open education platform to present music teaching videos. Three main features were presented including music lesson explanations, instrumental playing demonstrations, and live music performances. The course contained four major themes and a total of 97 steps. Using the testing data provided by the education platform, we analyzed the performance of learners to understand how well the course was taught using three test data in the statistics dashboard including course-run measures, total statistics, and statistics by week. We reviewed the course's star rating in a one-year run. The result of teaching the course needs to be evaluated and adjusted in a future study.

A decision-support model through online reviews: Consumer preference analysis and product ranking

Online reviews have become an important reference for consumers when choosing and purchasing products (or services); however, previous research based on these reviews has overlooked consumer preferences at the attribute level, as well as product ranking at the regional level. Here, we propose a decision-support model through online reviews involving 4 modules: (i) data collection and preprocessing, (ii) transformation from star ratings into probabilistic linguistic term sets (PLTSs), (iii) consumer preference analysis, and (iv) product ranking. After data collection and preprocessing, star ratings are represented as probabilistic linguistic term sets. Then, the most satisfactory product attribute and the least satisfactory product attribute are extracted from text comments using the well-known term-frequency-inverse document frequency (TF-IDF) algorithm. Thereafter, a data-driven best-worst method, in which preferences are given by objectively quantifying multiple product attributes based on star ratings and the weightage score obtained from the TF-IDF results, is established to analyze consumer preferences. Furthermore, product ranking is investigated from two levels: a region-based ranking method to discuss product ranking over different regions and a ranking position aggregation method to discuss product overall ranking based on a 0-1 integer programming model. We verify the decision-support model with a dataset of 10987 reviews of 20 automobile models from the Autohome platform. Some comparisons, theoretical and practical implications are provided.

Occupational Injuries in the US Nursing Homes.

Workplace injuries adversely affect worker well-being and may worsen staffing shortages and turnover in nursing homes. A better understanding of the trends in injuries in nursing homes including organizational factors associated with injuries can help improve our efforts in addressing worker injuries. To summarize the trends in injuries and organizational correlates of injuries in US nursing homes. We combine national injury tracking data from the Occupational Safety and Health Administration (2016-2019) with nursing home characteristics from Nursing Home Compare. Our outcomes include the proportion of nursing homes reporting any injuries, the mean number of injuries, and the mean number of injuries or illnesses with days away from work, or job transfer or restriction, or both (DART). We descriptively summarize trends in injuries over time. We also estimate the association between nursing home characteristics and injuries using multivariable regressions. We find that approximately 93% of nursing homes reported at least 1 occupational injury in any given year. Injuries had a substantial impact on productivity with 4.1 DART injuries per 100 full-time employees in 2019. Higher bed size, occupancy, RN staffing, and chain ownership are associated with increased DART rates whereas higher overall nursing home star ratings and for-profit status are associated with decreased DART rates. A high proportion of nursing homes report occupational injuries that can affect staff well-being, productivity, and quality of care. Injury prevention policies should target the types of injuries occurring in nursing homes and OSHA should monitor nursing homes reporting high and repeated injuries.

Development and Validation of the Meiji Nutritional Profiling System (Meiji NPS) to Address Dietary Needs of Adults and Older Adults in Japan.

This study introduces the Meiji Nutritional Profiling System (Meiji NPS), which was specifically designed to respond to age-related shifts in nutrient requirements among Japanese adults (<65 years old) and older adults (≥65 years old). Japan has one of the most aged societies in the world. The health issues of interest are malnutrition and lifestyle-related diseases among adults and frailty among older adults. Two versions of the NPS were developed based on nutrients to encourage (protein, dietary fibers, calcium, iron, and vitamin D), food groups to encourage (fruits, vegetables, nuts, legumes, and dairy), and nutrients to limit (energy, saturated fatty acids, sugars, and salt equivalents). The Meiji NPS for older adults did not include iron or saturated fatty acids. The algorithms were based on the Nutrient-Rich Foods Index (NRF). The convergent validity between the Meiji NPS and the existing NPSs for the same foods was confirmed using Spearman's correlation coefficients (NRF: r = 0.67 for adults and r = 0.60 for older adults; Health Star Rating: r = 0.64 for adults and r = 0.61 for older adults). The Meiji NPS may be useful for nutritional evaluation and reformulation of food products, tailored to adults and older adults to ameliorate health issues in Japan.

Research on Hotel Rating and Development of Star Rating in China

The hotel industry plays an important role in promoting economic growth, creating employment opportunities and providing high-quality services. Hotel rating helps to improve the service quality and market image of the tourism industry. Hotels in China use star rating methods. Since 1988, when the star rating standards were formulated, they have undergone many explorations and practices according to the development of the hotel industry. This study focuses on the development history of China's star rating as well as the current situation. It also finds that there are problems in China's hotel star rating standards, such as lack of adaptability, insufficient fairness, limitations, lack of legal regulation, and homogenized competition. Therefore, it is proposed to put forward corresponding countermeasures in terms of adaptability, transparency, professional quality, laws and regulations of hotel star rating standards, hoping to have a certain reference value for hotel star rating, and at the same time promote the overall development of the hotel industry.

Exploring the Importance of Destination Attributes of Sustainable Urban Waterfronts: Text and Data Mining of Tourists’ Online Reviews

This study identifies the destination attributes of sustainable urban waterfronts that are frequently mentioned in tourists’ online reviews. We analyzed the influence of these attributes on tourists’ ratings based on stimuli–organism–response theory, and the associations between these destination attributes. The online reviews (both text reviews and star ratings) from TripAdvisor and Google Maps of the sustainable waterfront destinations of the Liuchuan and Luchuan rivers in Taichung city (Taiwan) were collected and analyzed through text and data mining. Destination attributes were grouped into two types: sustainable landscapes (aesthetics, water resource rehabilitation, sustainable lighting, emotional experiences, and low-impact development waterfronts) and sustainable recreational spaces (leisure activities, festivals, inclusive destinations, photography, and tourist experiences). Two destination attributes common to-- both types were identified: nightscapes and waterfronts. These attributes predicted tourists’ ratings through support vector machine analysis. Sensitivity analysis revealed that sustainable landscape-type attributes had a greater impact on tourists’ ratings than the sustainable recreational space type. In addition, three important association rules between twelve attributes were identified and these helped provide information pattern combination attributes from tourists’ comments with support and confidence for the destination attributes. These findings will contribute to urban planning and design in relation to sustainable waterfront destinations. They highlight the need for planners to consider both tourists’ landscapes and recreational needs in order to achieve economic and ecological sustainability.

Application of sentiment and word frequency analysis of physician review sites to evaluate refractive surgery care

BackgroundOnline physician reviews increase transparency in health care, helping patients make informed decisions about their provider. Language processing techniques can quantify this data and allow providers to better understand patients' experiences, perspectives, and priorities. The objective of this study was to assess patient satisfaction and understand the aspects of care that are valued by patients seeking refractive care using sentiment and word frequency analysis. MethodsWritten reviews and Star ratings for members of the Refractive Surgery Alliance Society practicing in the United States were collected from Healthgrades, a popular physician rating website. Surgeons with at least one written review were included in the study. Reviews were scored from -1 (most negative) to +1 (most positive) using Valence Aware Dictionary sEntiment Reasoner (VADER). Reviews were stratified by demographic characteristics, namely gender, region, and years in practice. Word frequency analysis was applied to find the most common words and phrases. ResultsA total of 254 specialists and 3104 reviews were analyzed, with an average of 4.4/5 stars and mean 48 ratings each. Most physicians had positive reviews (96%, average VADER ​= ​0.69). Younger physicians (<20 years since residency) had significantly higher Stars rating than senior peers (>20 years) (P ​< ​0.001). A similar trend was observed in VADER score (0.71 vs 0.69), although not statistically significant (P ​= ​0.06). No statistical differences were observed between Stars rating and VADER score by gender (P ​= ​0.66, P ​= ​0.83) or by geographical region (P ​= ​0.74, P ​= ​0.07). "Staff" (n ​= ​1269), "professional" (n ​= ​631), "office" (n ​= ​523), "questions" (n ​= ​424), and "friendly" (n ​= ​386) were frequently used in reviews, along with phrases such as "the staff" (n ​= ​273) and "my questions" (n ​= ​174). "Surgery" (n ​= ​719), "staff" (n ​= ​576), "procedure" (n ​= ​251), "experience" (n ​= ​243), and "professional" (n ​= ​240) were the most common words in positive reviews, while "surgery" (n ​= ​147), "office" (n ​= ​86), "staff" (n ​= ​54), "time" (n ​= ​47), and "insurance" (n ​= ​28) were the most commonly used in negative reviews. ConclusionsBoth the average Stars and VADER sentiment score suggest a high satisfaction among refractive patients. Word frequency analysis revealed that patients value non-clinical aspects of care, including interactions with staff, insurance coverage, and wait-times, suggesting that improving non-clinical factors could enhance patient satisfaction with refractive surgery.

Nutritional values of ready-to-eat snacks available in the Indian E-market—a comparative study based on the health star rating system

BackgroundThe popularity of ready-to-eat (RTE) foods has increased globally, especially among children, because they are readily available, convenient, tasty, and have attractive packaging designs. The number of packaged foods, drinks, and snacks has risen in the Indian market, and the presence of less-healthy packaged products is a significant cause of overweight and diseases related to diet. The Health Star Rating (HSR) System, initiated by the Australian and New Zealand governments, was designed to rank these packaged products based on fat, sugar, energy, salt, and other nutritional compositions. The study assessed the nutritional value of RTE snacks currently available in the Indian e-market using the HSR system.MethodsThis comparative study analyzed the nutritional values of RTE snacks from November 2022 to February 2023. The data were extracted from the Indian e-market, namely Amazon Fresh, Flipkart Grocery, and Big Basket, using specific keywords. The HSR system has been used to evaluate and compare the healthiness of RTE snacks, using a star rating scale of 0.5 stars = bad to 5 stars = excellent. The study assessed 294 RTE products, including chips, cocoa-based snacks, and baked snacks, with a price range of INR 50 and below.ResultsThe findings revealed that the mean HSR was low (1.46) stars [SD 0.63] out of (5) stars for the studied products, with significant concerns about high levels of energy, sugar, sodium, and saturated fat. Additionally, the study observed a limited utilization of fruits, vegetables, nuts, and legumes (FVNL) in the market products.ConclusionThe study highlights the unhealthiness of snack products available in the Indian e-market due to high levels of sugar, salt, and fat. These snacks also lack FVNL as nutritious ingredients. The data extracted can underscore public health concerns, highlight the importance of consumer awareness, and help snack manufacturers develop healthier snacks for society.