1,720,954 research outputs found
Vulnerable Neighborhoods Had Smaller Gains in Health Insurance Coverage During Early Covid-19
Get PDFBackground:
The Covid-19 pandemic has illuminated the inequities that jeopardize health, prompting rapid interventions to promote care access. However, such efforts have had mixed success in eradicating disparities. One focus is the equity of improvements in insured rates.
Methods:
Self-reported health insurance coverage rates among adults aged 18-64 were obtained from the CDC’s PLACES datasets for collection years 2018 and 2021, at the Census Tract level. These data were merged with the CDC’s 2018 Social Vulnerability Index (SVI), which ranks Tracts on 15 measures of social determinants of health. Across Tracts nationally and by state, univariate correlations between Overall SVI and percentage change in uninsured rate from 2018 to 2021 were calculated. Then, national and state-specific multivariate models were created to assess the relationships between all 15 SVI variables and change in uninsured rate.
Results:
Health insurance coverage improved in all states. Nationally and across states, improvements were greater in lower-risk Census Tracts (34% average decrease in uninsured rate in lowest-risk decile nationally, vs. 16% in highest-risk decile, p \u3c 0.0001). Most state-specific multivariate models demonstrated high correlations between SVI variables and change in uninsured rate, with mean Multiple R of 0.74 (0.72 to 0.76 95% CI, F \u3c 0.0001 in all states).
Conclusions:
Nationally and in each state individually, pandemic-era improvements in health insurance access were disproportionately concentrated in lower-risk Census Tracts, which already had lower baseline uninsured rates. Multivariate models using SDOH inputs reliably identified vulnerable neighborhoods with lower health insurance access and smaller improvements in insured rates
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Differences in Self-Reported Cancer Measures Persist Despite Covid-Era Improved Insurance Access
Get PDFBackground:
The Covid-19 pandemic has challenged healthcare access, jeopardizing cancer-related health measures. However, pandemic-era health policies led to increased health insurance coverage across most counties in the United States. This study aims to explore relationships between uninsured rates and cancer prevalence and screening behaviors.
Methods:
Data were collected from the CDC’s PLACES datasets for the 2021 (collected 2018-2019) and 2024 (collected 2022) release years and merged at the county level. For both 2021 and 2024 releases, Pearson’s correlation and student’s t-test were performed between uninsured rates and the age-standardized rates of the following at the county level: cancer prevalence, colorectal cancer screening, and mammography. Percentage and percentage-point changes in county age-standardized prevalence from the 2021 to 2024 reports were then calculated for the same variables, after which Pearson’s correlation was determined for relationships by rate of change.
Results:
The median uninsured rate among adults aged 18-64 decreased from 16.5% to 10.5% from 2019 to 2022. Negative correlations between uninsured rate and cancer measures remained steady or worsened as follows: The correlation between uninsured rate and self-reported cancer prevalence strengthened from borderline weakly negative in 2019 (R = 0.20, p \u3c 0.001) to moderately negative in 2022 (R = 0.58, p \u3c 0.001). The correlation between uninsured rate and colorectal cancer screening remained strongly negative (R = 0.64 in 2018-2019, R = 0.61 in 2022, p \u3c 0.001 both years). The correlation between uninsurance and mammography strengthened from weakly negative (R = 0.28 in 2018-2019, p \u3c 0.001) to borderline moderately negative (R = 0.40 in 2022, p \u3c 0.001). There was a moderately positive correlation between percentage point change in uninsured status from 2019 to 2022 and change in self-reported cancer prevalence from 2019 to 2022 (R = 0.48, p \u3c 0.001), but relationships with changes in colorectal cancer screening and mammography were negligible.
Conclusions:
The persistence of negative relationships between county uninsured rates and cancer screening measures indicates the need for further promotion of screening access in at-risk communities. The counterintuitive inverse relationship between uninsurance and self-reported cancer prevalence requires further assessment given the risk of underdiagnosis in counties with lower screening rates. These challenges are exacerbated by the 2023 expiry of pandemic-era emergency policies to promote health insurance access, necessitating ongoing inquiry as new data on uninsurance are released
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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