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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.

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.

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.

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 ten percent 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.

It is argued that the Legitimate Violence Index (LVX) used by Murray A. Straus & Larry Baron (see SA 37:1/89U3029) to explain higher rates of homicide in the US South is not independent of geographic region or a measure of cultural acceptance of violence, as it is confounded with population density; further, eight of the twelve indicators of LVX have validity problems, & the construction of LVX is flawed. Failure to include a density variable in LVX makes research results suspect, since a recalculation leads to different results. In The Strength of Weak Indicators: Response to Gilles, Brown, Geletta, and Dalecki, Straus & Baron (U of New Hampshire, Durham) disagree with the comments, address the alleged indicator coding errors, defend the validity of the indicators, & maintain that consideration of the theoretical importance of population density leads to findings consistent with their theory. 3 Tables, 9 References. M. Malas

Doc number: 217 Abstract Background: Feldenkrais Method® teachers help students improve function and quality of life through verbally and manually guided lessons. The reasons people seek Feldenkrais® lessons are poorly understood. Similarly, little is known about practice characteristics and patterns. To address these knowledge gaps, we conducted an extensive survey of United States Guild Certified Feldenkrais Teachers®. Methods: We invited all Feldenkrais Teachers to participate in this survey delivered in web-based or print formats. We obtained overall and question-specific response rates, descriptive statistics, chi-square tests of response bias, and performed qualitative thematic review of comments. Results: Overall response rate was 30.5% (392/1287). Ninety percent of responders had college degrees in diverse fields; 12.5% had credentials outside health care, 36.9% held conventional health care licenses, and 23.1% had complementary and alternative medicine credentials. Mean age was 55.7 years; most teachers were women (83%). California (n = 100) and New York (n = 34) had the most teachers. Forty-five percent of teachers earned ≤ 20% of their gross income from their practices, while 26% earned > 80%. Most saw < 10 students/week for individual lessons and < 10 students/week for group lessons. Students were mostly women (71.1%) and 45-64 years old. The primary reason students sought Feldenkrais lessons was pain. A quarter of students self-referred, a fifth were referred by conventional health care providers, and two-thirds paid for services directly. Themes from comments included: beliefs that Feldenkrais training had important personal and professional benefits for teachers; recognition of the challenges of operating small businesses and succinctly describing the Feldenkrais Method ; the variety of practice approaches; and a deep commitment to the Feldenkrais Method . Conclusions: Most Feldenkrais Teachers were well educated, often held additional credentials, were located in the West, were women, were older than 50 years, and had part-time practices. Most students were women, were adults, came from various referral sources, and paid directly for services. Teachers and students utilized the Feldenkrais Method in diverse settings and applications. These findings may foster practice development by Feldenkrais Teachers , improve communication between health care consumers and providers and assist decision-making, and stimulate more research concerning the Feldenkrais Method .

This study examines the relationship between employment structure and poverty rate in non-metropolitan counties in the United States. Numerous studies that have examined the relationship between farm structure and local well-being in rural places have found some linkage between these variables. The recent trend of rural industrialization and the diversification of rural industries presents an opportunity to include all industries when investigating the relationship between industry structure and local well-being in rural places. An employment based index of industry concentration (using 4-sic industry code), was constructed for all counties classified as non-SMAs, to examine the relationship between employment structure and local well being. County poverty rate was used as an index of well-being. Regression equation is employed to model the relationship between the index of concentration and county poverty rates. In recognition of the importance of regional variation in the relationship between farm structure and local well-being as identified by previous research, five regional models were employed. The result indicated that places where a few employers provide most employment have a higher poverty rate as compared to places where many employers provide employment in a locality. Exceptions to this pattern of relationship is observed among counties in the Midwest (especially those that heavily depended on farming in this region), and in some parts of the Northeast.

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

Gilles, Geletta, Lobao, Schulman and Swanson criticize Barnes and Blevins' (1992) study of farm structure and economic well-being in nonmetropolitan areas. Barnes and Blevins respond to their criticism.