Explore the vast range of titles available.

Abstract Consumer-generated content from social media is an important component for decision making on long-term services and supports. Yet, the use and availability of such information remains unknown. The goal of this presentation is to evaluate and compare consumer sentiment about the services they received in the US. We used the most recent Yelp! Data - “Yelp Dataset Challenge” data (released January 18. 2018) and subset all businesses that are labeled as “long term care” or “nursing home” under the “business categories” field (n=127). Sentiment scores were extracted from the reviews (n=821) written by 821 unique individuals. The scores were grouped based on the source from which the scores were given (service recipients vs. designated surrogates). Our analysis shows that customer ratings targeted some specific services. Comments also included satisfaction with employees. Most providers receive positive ratings (mean =3.2 +/- 1.2). What factors are most important to consumers will be discussed.

This article addresses farmers' decisions to try using banded herbicide applications as well as factors that affect whether or not trial attempts are then extended to regular usage. The data is drawn from a total of 722 person-to-person interviews held in 16 Missouri counties, including 75 longer semi-structured interviews within three watersheds. The group of farmers who have tried banding operate significantly larger corn and soybean acreages, and have statistically higher levels of gross sales, education, knowledge of pesticides, and ability to apply their own chemicals. Logistic regression analysis suggests college education, certification as a private applicator, and gross sales as the three variables most likely to predict experimentation. Maintenance of the practice following initial use, however, is negatively related to farm size, and positively related only to gross sales and certification. Logistic regression analysis suggests only gross sales as significantly increasing the odds of adoption. The qualitative research reveals that obstacles with adoption for trial users center largely around difficulties of locating custom applicators for banding, the time and labor required for cultivation, and the ripple effects of banding-related tasks on other aspects of operator farming systems. In essence, banding satisfies farmers' desires to reduce pesticide use and protect water quality, but as a practice it is very difficult to incorporate into individual farming systems.

Sociologists have increasingly turned to secondary data sources to study social and economic change. As our experience in this area increases, so should our methodological rigor. Three common methodological sins-model mis-specification, inattention to regional influences, and fuzzy operationalizations-are illustrated through a critique of Barnes and Blevins' (1992) study of farm structure and economic well-being in nonmetropolitan areas. This paper argues, and demonstrates empirically, that many of Barnes and Blevins' conclusions are suspect because they did not include ethnic and regional variables in their regression studies

Employing primary data collected in the summer of 1991 in a representative survey of two farming areas in the territory of the republics of Russia and Ukraine, this study addresses the issue of the future involvement of collective and state‐farm workers in private farming. Through the use of a LISREL model, it is argued that those who have been involved in small‐scale private farming show no interest in expanding their farm operations or in buying or leasing additional land for farming. Moreover, it is maintained that those who intend to become farmers in privately owned and operated farms are more likely to be young, educated, and to some extent, ideologically committed to the free market system. A combination of structural constraints and a lack of knowledge regarding what to expect in the future can be viewed as possible explanations of the answers provided by respondents.

Claims that the Legitimate Violence Index is invalid because of problems with 8 of its 12 indicators and because it is confounded with region and population density. Includes a reply by Straus and Baron. (JLN)

Background Despite Ethiopia’s concerted efforts to eliminate malaria by 2030, the disease continues to pose a significant public health and socioeconomic challenge in the country. The year 2021 witnessed 2.78 million malaria cases and 8041 associated deaths, emphasizing the persistent threat. Monitoring the prevalence trend of malaria is crucial for devising effective control and elimination strategies. This study aims to assess the trend of malaria prevalence at the Metehara Health Centre in the East Shoa Zone, Ethiopia. Methods A retrospective study, spanning from February to September 2023, utilized malaria registration laboratory logbooks at Metehara Health Centre to evaluate the prevalence of malaria from 2017/18 to 2022/23. Malaria and related data were collected using a pre-designed data collection sheet. Descriptive statistics were employed for data summarization, presented through graphs and tables. Results Out of 59,250 examined blood films, 17.4% confirmed the presence of Plasmodium infections. Among the confirmed cases, 74.3%, 23.8%, and 1.84% were attributed to Plasmodium falciparum , Plasmodium vivax , and mixed infections, respectively. The trend of malaria exhibited a steady decline from 2017/18 to 2021/22, reaching 9.8% prevalence. However, an abrupt increase to 26.5% was observed in 2022/23. Males accounted for a higher proportion (66%) of cases compared to females (34%). The age group 15–24 years experienced the highest malaria incidence at 42%. Notably, malaria cases peaked during autumn (September to November) at 43% and reached the lowest percentage during spring (March to May) at 13%. Conclusion Malaria persists as a significant health challenge in and around Metehara, central Ethiopia, predominantly driven by Plasmodium falciparum . The five-year declining trend was interrupted by a notable upsurge in 2022/23, indicating a resurgence of malaria in the study area. It is imperative to adopt a reverse strategy to sustain the progress achieved by the national malaria control plan.

Purpose The purpose of this paper is to discuss the results of an effort to use social media generated data for measuring patient satisfaction with medical care services. Traditionally, scientifically designed patient satisfaction surveys are used to provide such measurements. The goal here is to evaluate the possibility of supplementing patient satisfaction surveys with social media generated patient satisfaction measurements such that the later can be used either as validation or replacement for the former. Although surveys are scientifically designed to yield dependable results, recent studies have revealed multiple factors relating to the methods currently used for survey data collection, that may be contributing to the limitations of many survey results. In light of such criticisms, this study explored the possibility of using the increasing popular and proactively generated consumer ratings through the pervasive social media as data source for satisfaction measurement. The average satisfaction scores created from such data are then used to compare levels of satisfaction among five types of health service businesses. Design/methodology/approach The data used in this research are garnered from the consumer review social media site called “Yelp!”. Ratings and reviews that are related to health and medical services were extracted from the “Yelp!” database. The types of services that are identified by consumers are standardized to typologies that are traditionally used in health service research. Five types of services were targeted – general practice physician offices, physician specialty services, dentists, hospitals and physical therapy services. The “five-star” rating systems were re-coded to form a five-point ordinal scale variable to represent “satisfaction score”. Findings The Yelp! data-based measurement of patient satisfaction produced an overall satisfaction score of 3.8 (SD=1.7) for the sampled services. The average satisfaction score per type of service ranged from 3.16 (SD=1.83) for specialty physicians to 4.52 (SD=1.57) for physical therapists. In general, dentists and physical therapists received higher average satisfaction scores as compared to the other medical services. Research limitations/implications Because this study was meant to evaluate the utility of social media generated data to measure satisfaction, in general, the estimates cannot be construed as representative of any underlying geographically defined population. They, however, do have a “cohort” interpretability. This limitation is not inherent to the use of the data source. If some geographically identifiable representation of the measurement data is desired, identifiable business data can be generated from the Yelp! system to specifically target relevant populations following the method that are tested in this study. Practical implications Under certain circumstances, such as the size and maturity of the gathered data, social media generated data can be a useful as a “fortuitous” alternative to satisfaction surveys for evaluating patient satisfaction with medical care. This is propitious as there have been some indication by studies that the advent of communication media in the twenty-first century may be undermining the reliability of scientifically designed surveys. Originality/value The use of social media generated data as “alternative” or “secondary” data source for research use is currently being widely investigated. To the author’s knowledge, this is the only paper that evaluated the use of “Yelp!” data as a possible source for population-based formal satisfaction measurement for healthcare services.

Background This study used natural language processing (NLP) and machine learning (ML) techniques to identify reliable patterns from within research narrative documents to distinguish studies that complete successfully, from the ones that terminate. Recent research findings have reported that at least 10 % of all studies that are funded by major research funding agencies terminate without yielding useful results. Since it is well-known that scientific studies that receive funding from major funding agencies are carefully planned, and rigorously vetted through the peer-review process, it was somewhat daunting to us that study-terminations are this prevalent. Moreover, our review of the literature about study terminations suggested that the reasons for study terminations are not well understood. We therefore aimed to address that knowledge gap, by seeking to identify the factors that contribute to study failures. Method We used data from the clinicialTrials.gov repository, from which we extracted both structured data (study characteristics), and unstructured data (the narrative description of the studies). We applied natural language processing techniques to the unstructured data to quantify the risk of termination by identifying distinctive topics that are more frequently associated with trials that are terminated and trials that are completed. We used the Latent Dirichlet Allocation (LDA) technique to derive 25 “topics” with corresponding sets of probabilities, which we then used to predict study-termination by utilizing random forest modeling. We fit two distinct models – one using only structured data as predictors and another model with both structured data and the 25 text topics derived from the unstructured data. Results In this paper, we demonstrate the interpretive and predictive value of LDA as it relates to predicting clinical trial failure. The results also demonstrate that the combined modeling approach yields robust predictive probabilities in terms of both sensitivity and specificity, relative to a model that utilizes the structured data alone. Conclusions Our study demonstrated that the use of topic modeling using LDA significantly raises the utility of unstructured data in better predicating the completion vs. termination of studies. This study sets the direction for future research to evaluate the viability of the designs of health studies.

The study examines the effects of the COVID-19 pandemic on different ethnic and racial groups. It aims to investigate the existence or nonexistence of significant variations in COVID-19 health outcomes among two ethnic and racial minorities that resided in Chicago neighborhoods during the onslaught of the pandemic. Researchers have traditionally studied health disparities by comparing the health of minorities representing \"underserved\" populations and those with adequate healthcare. This study focuses on the heterogeneity of health outcomes between different minority populations, mainly Black and Hispanic, traditionally considered underserved populations. This cross-sectional study uses secondary data from a public reporting site. The unit of analysis is neighborhood units based on US postal zip codes that are cross-referenced with the US Census Bureau's zip code tabulation area codes. We used Chicago neighborhood data and applied geographic analyses to describe the patterns of similarities and differences in the outcomes of the COVID-19 pandemic among neighborhoods with different ethnic and racial minorities residing in them. Using the one-way analysis of variance technique, we also tested research hypotheses about the COVID-19 outcome differences and/or similarities among the neighborhoods. Our findings show that although Hispanic neighborhoods disproportionately carried a higher burden of infection by the disease, the mortality due to the illness or the case fatality rate was not much higher than in the other neighborhoods. In contrast, African American neighborhoods did experience significantly higher case fatality rates-although their infection rate was not statistically significantly higher than the average infection rates of the other Chicago neighborhoods. Minority status creates distinct adverse effects on different minority groups. The patterns of distinct outcomes need to be well understood through further studied and considered by policymakers when health policies are designed to address the impact of health disparities.