dataesr/scientific-paragraphs-categorization huggingface dataset:
Because the χ 2 is sensitive to simple size, the use of relative goodness-of-fit measures is strongly recommended  [88]. The fit can be considered acceptable when the CFI and TLI are greater than 0. 90 and the RMSEA is equal to or smaller than 0. 08  [88, 89]. The three-factor solution of specific coping strategies showed a satisfactory fit to the data: χ 2 (39) = 117. 38, p = 0. 01; RMSEA = 0. 07; CFI = 0. 96; TLI = 0. 95; SRMR = 0. 06. Standardized factor loadings ranged from 0. 70 to 0. 91. To test the hypotheses, moderated hierarchical regression analyses using the Process Macro 2. 15 by Hayes  [90]  were performed through SPSS for self-related health and life satisfaction separately. The predictor (i. e. , job insecurity) and the moderator variables (i. e. , emotion-avoidance coping, problem-focused coping of the type job, social support and training search behaviors and problem-focused coping of the type organizational citizenship behaviors) were centered before calculating the interaction term by multiplying the predictor by each moderator  [91, 92].
Analytical Sample The final analytic sample included 3961 households who provided data on grocery purchases. From the original sample (n = 4826), we excluded households who did not report data on grocery purchases (n = 459), households <10th percentile (n = 375) or >99. 5th percentile (n = 24) of purchased items per week, households purchasing unidentifiable items (n = 6), and a single outlier whose total energy purchased exceeded the second greatest value by >15 times.
Healthy Eating Index 2015 The diet quality of grocery purchases was evaluated using the HEI-15 (range 0-100 points), with higher scores representing greater concordance with US Dietary Guidelines  [19] [20] [21]. HEI-15 total and component scores for nine adequacy and four moderation components were computed using publicly available SAS code from National Cancer Institute  [22]. Previous research suggests that a 5-point difference in HEI scores can be considered as clinically meaningful  [11].
Data was rigorously collected over a 7-day period and linked with appropriate USDA food codes so that HEI-15 scores could be calculated. Because grocery purchase data is difficult to link with a nutrient database, the FoodAPS survey provides a unique opportunity to rigorously investigate the diet quality of grocery purchases. In conclusion, the dietary quality of grocery purchases varies in relation to race/ethnicity and geographic region in a nationally representative sample of US households. These disparities exist despite holding socioeconomic factors and education constant, underscoring the importance of addressing the higher levels of the social-ecologic model (i. e. , macro-level environments (sectors), and settings)  [43]  when trying to modify determinants of food choice. The remarkable consistency of association between studies examining individual-level diet quality and household-level diet quality further highlight the promise of using grocery purchase data as an ongoing surveillance tool to monitor the diet quality of households.
The dataset contains 1042 records obtained from inpatients at hospitals in the northern region of Vietnam. The survey process lasted 20 months from August 2014 to March 2016, and yielded a comprehensive set of records of inpatients' financial situations, healthcare, and health insurance information, as well as their perspectives on treatment service in the hospitals. Five articles were published based on the smaller subsets. This data article introduces the full dataset for the first time and suggests a new Bayesian statistics approach for data analysis. The full dataset is expected to contribute new data for health economic researchers and new grounded scientific results for policymakers.
the patient or their family. Eventually, 1042 records were collected. Smaller subsets have been derived from the dataset and analyzed to explore health insurance issues  [1] , health care payments, financial destitution  [2] [3] [4] , and satisfaction with healthcare services  [5]. The submitted dataset provides the full 1042 observations and the entire set of coded variables. Moreover, a demo analysis of a Bayesian statistics approach is also introduced in the article. The comprehensive information from the dataset and the new method are expected to provide resources for health economic researchers to investigate the healthcare and health insurance services in transitional economies such as Vietnam. In the Data Description section, we explain in detail the coded variables and propose some potential research questions that might be explored using the dataset.
Data Description The dataset includes 1042 records of patients' demographic information, financial status, opinions about treatment, and hospital fees. Previously, smaller datasets of 330 and 900 records extracted from this dataset were used to explore health insurance and healthcare services  [1, 2, 5]  in addition to the financial burden of patients  [2] [3] [4]  in Vietnam. The current dataset, never publicized before, presents all of the records with all measured variables. There are 15 categorical (discrete) variables and 15 numerical (continuous) variables. Some of these variables could be used indirectly. For instance, the numerical variable "Income" was used to constitute "IncRank". Details of the categorical variables can be found in Table  1.
Illness The seriousness of the patient's illness or injury. In the dataset, the variable "Ill2" combined two values "ill" and "light" into one value "light" for analysis. In Figures  1  and 2 , visualizations of the variables "Burden" and "IfHigher" are shown. Figure  1  confirms the intuitive observation that lower-income patients tended to have a higher financial burden, while the total medical expenditures and daily costs rose according to the degree of the financial burden. This result indicated a finance-health dilemma for low-income patients in Vietnam. Figure  2  shows that the income of male patients was relatively higher than that of female patients, while the total medical expenditures and average daily costs for both males and females were relatively similar. The implication is clear: female patients faced a greater financial risk than their male counterparts.
As a result, the risk of being destitute seems to be higher among poor households  [9]. This dataset can, therefore, provide evidence and trends regarding the financing methods of Vietnamese patients in health services. Table  3  shows some potential research questions and hypotheses that can be examined by employing this dataset. Several research questions and hypotheses have already been explored using smaller datasets  [1] [2] [3] [4] [5]. • What are the effects of socio-demographic factors on the probability of being destitute? • To what extent are socio-demographic factors the determinants of the degree of illness? • What is the impact of hospitalization length on patients' financial burden? • How do the treatment costs and illness explain the end outcome of treatment? • How does the amount of out-of-pocket "extra thank-you money" determine the end outcome of treatment? The survey took 20 months to finish due to the sensitive nature of the research.
For instance, there were cases in which the survey team had to approach the patients or families four to five times over the course of four weeks in order to collect one questionnaire. As a matter of fact, some patients themselves or their family members became too emotional to finish the survey as they thought of the severity of their illnesses. Raw data from the collected questionnaires were entered into an Excel file at 1042data. xlsx (see the dataset). The data were then edited and saved in CSV format for analyzing in the R statistical software (v3. 5. 3). Both frequentist and Bayesian statistics approaches were explored in the data analysis.
Frequentist Analysis The analysis used the baseline-category logits (BCL) model  [10]. Because the current dataset was a combination of discrete and continuous variables, logistic regression was a suitable method for demonstrating the independence or association among variables. Using coefficients, the logistic model could estimate the probability for each value of response variables according to the condition of the exploratory variables. The common equation of the logistic model is as follows: log π j (x) π J (x) = α j + β T j x, j = 1,. , J -1, where π j (x) = P(Y = j x) , with Y as the response variable, indicates the probability corresponding to the exploratory variable x. The probability of each response variable was calculated as follows: π j (x) = exp α j + β T j x 1 + J-1 h-1 exp(α h + β T h x).
The current article employs the analysis used in  [2] , which estimated the probability of the type of Burden by using the 330-observation dataset. This time, the model was re-run using the full 1042-observation dataset. Table  4  reports the results obtained from the estimations. The analysis was executed by using the following R commands: > library(nnet) > library(stargazer) > data1$Res<-relevel(data1$Res,ref="Yes") > data1$Insured<-relevel(data1$Insured,ref="Yes") > logit_burden<-multinom(Burden ~Res + Insured, data=data1) > stargazer(logit_burden,type = "text", out = "logit_burden. htm") Additional R commands can be found in CodeR. txt (see the dataset). The resulting coefficients were then used to construct Equations (  1 )-(  3 ), corresponding to each logit model respectively, as follows: log πB πA = -1.
291 + 1. 784NonRes + 1. 601UnInsured, ( 1 ) log πC πA = -2. 599 + 3. 801NonRes + 1. 635UnInsured, ( 2 ) log πD πA = -6. 561 + 4. 163NonRes + 2. 401UnInsured. ( 3 ) The probabilities corresponding to the status of burden outcomes were also calculated according to each condition of residency and being insured. The results are demonstrated in Figure  4 :  This dataset indicated a similar decreasing trend of probabilities of destitution corresponding to both long-time and short-time hospitalization (see Figure  5 ). It also confirmed that longer length of hospital stay increased the risk of falling into destitution  [5] : This dataset indicated a similar decreasing trend of probabilities of destitution corresponding to both long-time and short-time hospitalization (see Figure  5 ). It also confirmed that longer length of hospital stay increased the risk of falling into destitution  [5] :.
Bayesian Analysis In this section, we use a Bayesian statistics approach to examine the dataset. We hoped that the application of Bayesian statistics would bring a fresh perspective to the dataset. The strength of the Bayesian approach is its capacity to visualize the result and the distributions of the coefficients. Moreover, the Bayesian approach also allows for a robustness check of the model using the analysis of prior sensitivity. Had the model been not sensitive to adjustment of the prior, we would have robust evidence for its credibility  [11] [12] [13] [14]. R statistical software and a BayesVL package (v0. 6) were used to construct a regression model for the correlation between the patients and their families' financial situation after paying for treatment ("burden") against where the patients reside ("res") and whether they were insured or not ("insured")  [13] [14] [15] [16].
Similar applications of Bayesian statistics can be found in  [11, 12]. The BayesVL package is available in  [17]. The mathematical formulation of the model is as follows: burden[i] = α + β_res * res[i] + β_insured * insured[i]. The BayesVL package (v0. 6) was used to design the model, generate the STAN code for the model, and for the test. Examples of R code that were used to construct the model are as follows: # Design the model model <-bayesvl() model <-bvl_addNode(model, "burden", "norm").
Similar applications of Bayesian statistics can be found in  [11, 12]. The BayesVL package is available in  [17]. The mathematical formulation of the model is as follows: burden [i] = α + β_res * res[i] + β_insured * insured[i]. The BayesVL package (v0. 6) was used to design the model, generate the STAN code for the model, and for the test. Examples of R code that were used to construct the model are as follows: In the mathematical form: burden ~4. 08 -1. 03 * res -0. 33*insured. As shown above, all regression coefficients were negative, which suggested that where patients reside would affect their financial burden after paying for treatment, while having insurance showed less effect on the financial burden. The posterior distribution of all coefficients is presented in Figure  6. As shown in Box 3, all regression coefficients were negative, which suggested that where patients reside would affect their financial burden after paying for treatment, while having insurance showed less effect on the financial burden.
Conclusion This data descriptor article presents a comprehensive dataset on the situations and opinions of inpatients regarding the cost of treatment at the hospital, and the application of both the frequentist and Bayesian statistics approaches in data analysis. Smaller subsets extracted from this dataset were the backbone of five different health economic publications, which contributed significantly to the literature of healthcare, health insurance, patients' satisfaction with the hospital, and their financial destitution. The public availability of the full dataset and the introduction of Bayesian method will.
Conclusions This data descriptor article presents a comprehensive dataset on the situations and opinions of inpatients regarding the cost of treatment at the hospital, and the application of both the frequentist and Bayesian statistics approaches in data analysis. Smaller subsets extracted from this dataset were the backbone of five different health economic publications, which contributed significantly to the literature of healthcare, health insurance, patients' satisfaction with the hospital, and their financial The public availability of the full dataset and the introduction of Bayesian method will enable health economic researchers to explore more issues and infer significant insights. Furthermore, the previous and upcoming findings based on this dataset have supported and will continue to inform the decisions of healthcare policy-makers in making grounded policies that will help inpatients  [18].
We acknowledge that the dataset only reflects the situation in the northern region of Vietnam and the mindset of people in this region. In different areas with different economic contexts, specific findings may not hold. However, the values of this dataset do not only lie in its records, but also the design logic, the usage of coded variables, and the potential for replication and expansion. Therefore, we hope scholars from Vietnam and worldwide will breathe new life into the dataset. We believe researchers from different backgrounds will be able to exploit every aspect of this dataset, under comparative perspectives, for example.
In order to collect the data, 1042 patients from a number of hospitals in the northern region of Vietnam were surveyed by questionnaires. The surveyed hospitals were major hospitals in the region, such as Viet Duc Hospital and Bach Mai Hospital in Hanoi, Viet Tiep Hospital and Kien An Hospital in Haiphong, and Uong Bi Hospital in Quang Ninh, to name a few. Further details can be seen in the dataset. The survey strictly conformed to the ethical standards of the ICMJE Recommendations, the WMA Declaration of Helsinki, and Decision 460/QD-BYT by the Vietnamese Ministry of Health. A total of 330 records were collected during the first phase, from 2014 August 10 to February 2015. More records were obtained from February to May 2015, raising the total number of observations to 900. The third and final phase ended in March 2016, with the final set of 1042 patient records.
Adverse Event Pattern of Herbal Medicines With respect to the general adverse event pattern of herbal medications, 1 study evaluated adverse effects reported in randomized controlled trials  [73]. The authors identified 111 studies of a single herb and 133 of multiple herbs that included in total 15,441 participants (male = 5,590; female = 9,851; 7,383 for single and 8,058 for multiple herb studies). Overall, 480 cases (3. 1%) of adverse events (344 for single, 136 for multiple herb studies; p < 0. 01) were recorded. The most frequently reported adverse event was digestive symptoms (44. 3%), followed by adverse events such as loss of appetite. Based upon this, the authors concluded that herbal treatments appear "relatively" safe  [73]. This conclusion was supported by a further systematic review  [74]  that searched 5 electronic databases to identify adverse effects of herbal medicines.
Paracetamol alone accounted for 45% of all liver failures that required liver transplantation. Similarly, based on recent data from the Spanish databases on drug-induced liver failure  [80] , 4% of cases were related to herbal and dietary supplements and recently data from the United States were published that reported 899 cases of definite, highly likely, or probable drug induced liver injury. In this case series, all herbals and dietary complements together were related to only 16% of observed cases of drug-induced liver injury  [81]. Thus, 84% were related to the chemically defined medicines. On the other hand, a report from Switzerland documented 10 cases of hepatotoxicity over a 7-year period related to one product line (Herbalife)  [82]. This may suggest that some herbal products are hepatotoxic and subsequently specific preparations available to consumers carry a higher risk.
A standardized approach is required to monitor and evaluate adverse events in patient cohorts and compare their incidence with the background incidence. The diagnosis of an adverse drug reaction and the determination of a causal relationship to a specific drug can be a complex task and even if diagnostic criteria for adverse drug reactions are rigorously applied is not without limitations  [92]. Another issue might be reporting bias  [93]  and attitudes of clinicians may unconsciously influence the reporting of adverse events. Rigorous research methodologies using large population-based health databases should help do address some of these limitations  [94]. Data linkage of medication data and the onset of specific predefined clinical events supported by large integrate electronic medical records  [95]  should enable clinicians and regulators to identify potential adverse events without clinical reporting bias.
National and international guidelines are mostly based on the database of randomized controlled trials (RCTs). In fact, the GRADE method (Grading of Recommendations, Assessment, Development and Evaluation) explicitly takes into account all types of study designs, from RCTs to observational studies and case reports  [37, 38, 39]. GRADE also considers data on preferences, acceptability and feasibility or accuracy of results. For the applicability of guidelines in the routine care of patients, the results of RCTs Step-up algorithm in treated patients (adolescents over 12 years and adults) based on visual analogue scales. The proposed algorithm considers the patient's preferences: If ocular symptoms persist after initiation of treatment, local conjunctival therapy should be added. Due to the characteristics in the German health care system of direct specialist access, the entire treatment chain from anamnesis, to allergen avoidance, pharmacological therapy, indication and implementation of AIT can also be performed by an allergologically competent specialist or a physician with additional training in allergology, which enables an early AIT.
A Case of Undone Science That Affects Lower-Status Workers Recent trends in science studies have emphasized that knowledge does not develop uniformly but that various social factors explain the production of both knowledge and ignorance  [31]. More specifically, some studies have highlighted the structural inequalities that can lead to the significant development of certain fields of knowledge and, on the contrary, to a lack of interest in other issues referring to it as "undone science" [32 though not specifically about PAFs and the occupational gradient of cancer. This knowledge gap starts with the irregular and incomplete description of occupational exposures to carcinogens, and it is made even wider by the few data documenting their social patterning. Workplace exposure databases usually rely on monitoring systems of uncertain coverage and sustainability  [39, 40].
Large initiatives such as the European CARcinogen EXposure database (CAREX)  [41]  and CAREX Canada  [42]  have provided nationwide estimates for the current number of workers exposed in different occupational groups. Other countries have instead conducted either one-time (Australia  [43] ) or repeated cross-sectional studies (France [44•]) to monitor the prevalence of hazardous exposures at work. The French SUMER surveys (SUrveillance Médicale des Expositions aux Risques professionnels) are among the few that allow quantifying exposure variations by occupational group (up to 28. 5% exposed to CMRs in skilled BCW); function (up to 36. 7% in installation, repair, and maintenance); and sex  [44• ]. Different trends are also observed for apprentices (e. g. , increasing from 22. 5 to 27. 2% between 2003 and 2010)  [45]  and a lower level of control is observed in unskilled workers  [46].
Acknowledgments  EHESP  ( Modalité Sept ,  2018 ) and  Fondation de France  for funding this research. Thanks to  Dominique Diguet  (Ined) and  Myriam Fayad (Ehesp)  for their precious help in reviewing the literature, to  Steve Stellman  and  Mary Beth Terry  at the  MSPH (Columbia University ) for their thoughtful conversations that nourished the writing of the present paper, and to  Christophe Paris  ( Inserm-Rennes 1 University ) for his careful reading.
e. a theoretical domain) to each BCT. The procedure was based on an online self-questionnaire allowing these linkages. On this basis we selected the linkage mechanism(s) -BCTs chosen by at least half of the experts -and proceeded to a second round asking the group to validate or adjust them. We again selected the linkage mechanism(s) -BCTs chosen by half of the respondents; iii) Thirdly, during the 2nd user seminar, we asked the user group to validate or adjust the linkage mechanism(s) -BCTs produced by professionals. They validated all of them and added several comments which we then added into the e-Delphi software program  [37] ; iv) Finally, in the 3rd round of the e-Delphi procedure, we asked the professional group to validate the list of linkage mechanism(s) -BCTs adjusted. They did so.
Acknowledgments  Remy Bataillon Cyril Crozet ,  Sylvain Fernandez-Curiel Vincent Dhyèvre Christelle Gabrielli Prof. Rémi Gagnayre ,  Prof. Pierre Lombrail ,  Cristina Lallement Marie Persiani Audrey Petit Dr. Michel Serin Sarah Vernier Marie Verzeroli Magalie  Léo The 12 volunteers from a cardiovascular disease prevention network.
Participant and experiments This retrospective single-case study involved an active male (41 yr, 1. 77 m, 96. 6 kg) with a right unilateral transfemoral residuum fitted with screw-type osseointegrated fixation eight years before the study  (Frossard et al. 2013). The participant walked in straight level walkway at self-selected speed fitted with a mechanical Total Knee (7 trials) and microprocessor-controlled C-Leg (8 trials) knees as well as 13 markers on the legs, arms, and trunk. Kinematic and dynamics data were captured with a 6-camera motion capture system (Qualisys) and two force plates (Kistler), respectively.