bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accounting Fundamentals and the Variation of Stock Price: Factoring in the Investment Scalability
Authors: Sumiyana Sumiyana , Baridwan Zaki , Sugiri Slamet , Hartono Jogiyanto 
Published: September 2018

This study develops a new return model with respect to accounting fundamentals. The new return model is based on Chen and Zhang (2007). This study takes into account theinvestment scalability information. Specifically, this study splitsthe scale of firm’s operations into short-run and long-runinvestment scalabilities. We document that five accounting fun-damentals explain the variation of annual stock return. Thefactors, comprised book value, earnings yield, short-run andlong-run investment scalabilities, and growth opportunities, co associate positively with stock price. The remaining factor,which is the pure interest rate, is negatively related to annualstock return. This study finds that inducing short-run and long-run investment scalabilities into the model could improve the degree of association. In other words, they have value rel-evance. Finally, this study suggests that basic trading strategieswill improve if investors revert to the accounting fundamentals.Keywords: accounting fundamentals; book value; earnings yield; growth opportuni­ties; short­run and long­run investment scalabilities; trading strategy;value relevance
Fraudulent Financial Statement: Evidence from Statement on Auditing Standard No. 99
Authors: Suyanto Suyanto 
Published: September 2018

The goals of this study are to empirically identify fraud risk factors and construct a model to predict the likelihood of financial statement frauds based on SAS No. 99. Employing logistic regression on 143 firms, this research finds that fraud risk factor proxies for Pressure—net profit/total assets—and Opportunity— inventory/total assets ratio, related party transactions, and Big 4—are significantly associated with fraudulent financial statements, whereas none of the fraud risk factor proxies for Rationalization is significantly associated with fraudulent financial statements. Consistent with prior research, it seems that the likelihood of fraudulent financial statements is easier to be observed publicly using fraud risk factor proxies for Pressure and Opportunity rather than Rationalization. The constructed model can correctly classify firms with a relatively high success rate.
INTRAGROUP COOPERATION VS. INTRAGROUP COMPETITION: A Meta-Analytical Study
Authors: Na'im Ainun 
Published: September 2018

This study examines whether intragroup cooperation outperforms intragroup competition. Intragroup cooperation is a work setting when individuals in a group perceive that their goal attainments are positively related; while intragroup competition is a work setting when individuals in a group perceive that their goal attainments are negatively related. Performance is defined as group productivity level, speed of solving problems, and quality of the group outcomes.Meta-analytical method is used to test the hypothesis. The method combines and compares eight studies from various research disciplines i.e., education, psychology, organization, and business and accounting. The studies being analyzed also vary in terms of the research and experimental setting such as manufacturing operations and problem solving games.Meta-analytical study is relatively rare in Indonesia, so that this study is important to introduce and to show the importance and the benefit of the analysis for concluding a large number and different research in a similar domain of research question. In a simple term, a meta-analysis is a quantitative literature review. However, the analysis has a more powerful procedure (than the conventional literature review) to indicate more clearly, and in quantitative terms, the consistency, the differences and similarities of previous studies.The author found that intragroup cooperation outperforms intragroup competition is held across different experimental subject educational backgrounds such as education, psychology, and business and accounting. However, the extent of the difference in performance is higher in the subjects with psychology background than that of the subjects with business and accounting background. The reason is that business and accounting subjects are more exposed to competitive environments than those whose backgrounds are psychology and sociology.
Investigating disparities in cancer by linking the Canadian Cancer Registry to survey and administrative databases: a collaboration between the Canadian Partnership Against Cancer and Statistics Canada
Authors: Bryan Shirley , Tung Jasmine , Chadder Jennifer , Louzado Cheryl , Decady Yves , Sung Yubin , Rahal Rami 
Published: September 2018

IntroductionThe Canadian Partnership Against Cancer reports on pan-Canadian system performance across the cancer control continuum, including how sociodemographic disparities create barriers in access and utilization of cancer control services. This has been done using mostly ecological data, which does not contain the individual level information required to identify the extent of disparities.
Objectives and ApproachThe Partnership collaborated with Statistics Canada (STC) to build individual level datasets that will allow researchers to investigate the relationship between sociodemographic factors, cancer outcomes and treatment patterns in Canada.The record linkage was conducted at STC within the Social Data Linkage Environment. Data from the Canadian Cancer Registry were linked to the Discharge Abstract Database, the National Ambulatory Care Reporting System and the Canadian Vital Statistics Death Database to obtain treatment information and deathand death outcomes. To obtain sociodemographic information the following datasets are also being linked: T1 Personal Master File (income), Immigrant Landing File and the Census Long Form (education and geography).
ResultsLinkage of all datasets is expected to complete by the end of January 2019. For the first time in Canada, record-level linkage of national cancer registry data with key datasets containing sociodemographic information will be available for exploratory analysis. The challenges with linkage and data limitations will be discussed, as well as the application of these linked databases to answer current disparities-related research questions.
Conclusion/ImplicationsThis initiative illustrates the value of collaboration between data custodians and health researchers as well as how linkage of existing datasets can leverage the full potential of available data, and broaden cancer research in supporting efforts to create a more equitable cancer control system.
If You Link It They Will Come, If They Like It They Will Stay: The Utah Population Database as a Model for Creating a Confidential Linked Population Health Research Registry
Authors: Smith Ken , Fraser Alison 
Published: September 2018

IntroductionThere is increasing demand for so-called big data for a range of uses. One of the challenges is the method by which these data are obtained, the identiers used for record linking, how they are linked, and the secure manner by which researchers may use these data ethically.
Objectives and ApproachThe Utah Population Database (UPDB) has a long history of linking records derived from genealogies, death/birth certificates, medical diagnoses, and census records. This infrastructure offers a unique resource for genetic, public health, geographic and demographic investigations. The UPDB now holds information on 11 million individuals linked into multigenerational pedigrees. New data opportunities have arisen that provide value added to UPDB but that raise important infrastructure and privacy issues. This presentation will provide details and strategies for dealing with new challenges of adding new and complex sources of data.
ResultsThe record linking and data security measures developed for the construction of the UPDB, we suggest, is a model for other states and provinces. The ability for UPDB to obtain, store, link and dispense a diverse set of data at the individual and genealogical level for the research community for over forty years suggests that it has elements that are potentially replicable for other locations. The development of a unique and independent regulatory body, the Resource for Genetic and Epidemiologic Research (RGE), which oversees access to UPDB data, speaks to this as a model for protecting the confidentiality of the data while enhancing secure to researchers. We show the technical, statistical, legal and regulatory methods that have provided data for nearly 300 distinct health-related studies.
Conclusion/ImplicationsThis study demonstrates how the careful and consistent application of secure data management and regulatory oversight allows for the development of the UPDB and its ability to link diverse data sources for the research community. The legal and administrative strategies used serve as a model for other states and provinces.
Meta Matters - Enriching & Exploiting Your Metadata
Authors: Hacker Alex 
Published: September 2018

IntroductionData is nothing without context: if you don't know how, when or why a variable was gathered, it's nigh impossible to draw conclusions from it. This presentation discusses different sorts of metadata and how they can be gathered, stored, and used to enrich data; drawing examples from our biobank.
Objectives and ApproachEach data item has two types of metadata: variable-level and value-level. For example, consider a questionnaire. The variable-level metadata covers each question: exact wording, validation rules for the answers, etc. The value-level metadata covers each individual answer: details of the questioner, date and time of response, and so on. We also have database-level metadata: datasets which list every dataset or every field in the database. While some of this information needs to be gathered alongside the data itself, much can be extracted or imputed from results or documentation. We present some generalizable examples.
ResultsLike any other data, metadata is only worth having if you’re using it. We will present principles and examples of applications that we have developed for it:

Data management – Deriving useful variables and tables, and helping to make your data easier to parse, extract, and validate.
Presentation – Making your data more human-readable by labelling variables and decoding values.
Documentation – Metadata tables make ideal repositories for granular institutional knowledge about your data: known issues, potential pitfalls, or explanations for missing values.
Analysis – Identifying which metadata variables are most valuable for analysts, and how best to provide them.
Automation – Using the metadata to generate code that can automatically produce summary statistics, tables, graphs… and more metadata!

Conclusion/ImplicationsEvery dataset comes with some metadata. When examined and built upon, it can deepen understanding of the data within, as well as becoming a powerful resource in its own right.
Housing and Health: Linking Population Health Survey Data to Housing Assistance Data
Authors: Golden Cordell , Mirel Lisa 
Published: September 2018

IntroductionThe linkage of survey data with administrative data enhances the scientific value and analytic potential of both sources of information. Combining multiple data sources facilitates richer analyses and allows data users to answer research questions that cannot be addressed easily using a single data source.
Objectives and ApproachRecently, the United States National Center for Health Statistics (NCHS) and Department of Housing and Urban Development (HUD) collaborated to link two population health surveys conducted by NCHS with housing assistance program data maintained by HUD. The resulting linked data files enable researchers to examine relationships between the receipt of federal housing assistance and health. In this talk, we will describe some of the challenges faced when initiating a data sharing agreement between two federal agencies governed by distinct legislative authorities, particularly issues related to legal requirements and data access.
ResultsWe will describe each of the data sources used in the linkage as well as the methodology used to combine the data. Lastly, the discussion will focus on the inter-agency collaboration that led to the production of the supporting technical documentation developed to assist researchers using the linked data files. The linkage of NCHS survey data and HUD administrative data serves as an example of how two agencies were able to overcome challenges to successfully form a data sharing partnership as a cost-effective means to develop a robust data source that benefits the collaborating agencies as well as policy makers and outside researchers.
Conclusion/ImplicationsBoth agencies anticipate that this partnership will continue as additional survey and administrative data are collected.
Validation & Vindication – Comparing Electronic Health Records with Hospital Notes
Authors: Hacker Alex 
Published: September 2018

IntroductionNo doubt your Electronic Health Records have been meticulously gathered, imported, validated and standardised. However, if you want to be certain that they are an accurate representation of reality, you can’t beat physically going to hospitals and cross-checking their records against yours. Our biobank did exactly this.
Objectives and ApproachOur validation exercise encompassed all reported cases in our follow-up data of three key conditions: stroke, heart disease, and cancer. Key data about each hospitalisation was extracted and exported to tablet computers running custom software. Our staff then visited each hospital in this dataset seeking the corresponding medical notes, and collected additional data from those that they found including photographs of key documents. These results were then adjudicated by specialist physicians to determine the accuracy of the diagnosis, and identify disease phenotypes of interest. Finally, all these results were merged back into our follow-up data.
ResultsNot only was gathering the data a huge logistical and technical challenge, integrating it back into the database presented its own difficulties. Our initial plan was to assign each sought event a status of ‘validated’, ‘corrected’ or ‘unfound’. However, this proved inadequate for addressing the complexities of the data, as we will discuss, with examples.
Our solution was to initially treat the retrieved hospital notes as simply another source of follow-up data. We were thus able to use our existing systems for validating, standardising and aggregating events; and thus produce validated endpoints that were meaningfully comparable to our reported endpoints. We could then implement and test definitions of the required validation statuses at a participant level for each disease of interest.
Conclusion/ImplicationsThis validation project was a huge and daunting undertaking, but repaid our investment with proof that our Electronic Health Records were generally very reliable, and also with much richer data about disease diagnosis and phenotyping. Other projects using Electronic Health Records may wish to adopt this approach.
The Economic Impacts of ICD-9 to ICD-10 Health Indicator Coding System Transition in the Calgary Region
Authors: Khair Shahreen , Lu Mingshan , Quan Hude , Doktorchik Chelsea , Eastwood Catherine 
Published: September 2018

IntroductionCoded data serves a critical part in the process of identifying the resource allocation required for each department in a hospital and for research purposes. This paper attempts a cost-benefit analysis of the transition from ICD-9 health indicator coding system to ICD-10 coding system and quantify the economic impacts.
Objectives and ApproachThe hypothesis adopted by this paper is that the transition from ICD-9 to ICD-10 has been beneficial for the health system due better disease management, resulting in cost savings and facilitation of high quality health research. Analyzing the inflation-adjusted costs compared with the benefits accrued from implementing the new coding system would enable informed decision making for the stakeholders at government and other levels of health provision.
The methodology involves constructing ‘benefit scenarios’ via analysis of existing literature and interviewing coding managers; costs are evaluated using data collected on re-training coders and productivity losses during the transition phase.
ResultsAn example of a benefit scenario would take the form of cost savings associated with correctly identifying people with diabetes (due to coded charts), hence resulting in a decline in blood sugar (HbA1c) levels via better disease management. This in turn may cause reductions in other high blood-sugar related diseases and thus increase efficiency for government funding in the health care sector. Improved data quality in ICD-10 is expected to have resulted in gains from specificity due to increased sensitivity of data classification and grouping. Actual cost of re-training of coders and ICD-10 software provider fees are expected to be higher than the costs anticipated before ICD-10 implementation. Productivity losses in the transition phase are expected to have declined as coders became more adept at coding.
Conclusion/ImplicationsAn economic evaluation proves to be a vital part of eliciting whether the transition to the newer method of coding, ICD-10, has been beneficial to the end users of the data. It is important to understand the efficiency of resource allocation to healthcare and the financial implications such investments entail.
A big data analytics platform to support simulation modeling for osteoarthritis care pathways
Authors: Vik Shelly , Sharif Behnam , Seidel Judy , Marshall Deborah A 
Published: September 2018

IntroductionTechnical solutions have been used in industry settings for many years to facilitate efficient management and analyses of big data sources. An initiative to apply a business solution to support development of simulation models for health systems research using nearly two decades of provincial administrative health data is described.
Objectives and ApproachAdministrative data including practitioner claims, hospitalizations and ambulatory care visits for patients with a diagnosis of osteoarthritis were obtained from Alberta Health for the period 1994/95 to 2012/13. These data were incorporated into a multidimensional data cube using Microsoft SQL Server Analysis Services. Initial steps required dimensional modeling to restructure the data into a star schema format. This involved appending several data sets and defining additional reference tables to contain stratification variables and denominator data for rate calculations. The modeling expert worked closely with the information technology team throughout the process and assessed validity of the output.
ResultsDevelopment and validation of the multidimensional cube occurred in iterations over approximately 12 months. The final solution resulted in an analytics platform that compiled data from approximately 400 million records obtained from four different administrative data sources. Ten dimension tables containing 102 variables provided enhanced flexibility to conduct ad hoc stratified analyses in a fraction of the time that would be required using conventional methods. For example, some analyses that previously required a day of analyst time could be performed in less than 15 minutes. The efficiencies in analytic time were achieved by the pre-aggregated measures and slice and dice capability of the data cube, which negated many intermediary steps for data extraction and time consuming iterative analyses required for development of the simulation models.
Conclusion/ImplicationsThis project demonstrated how a technical solution applied in industry can be utilized to address challenges encountered by researchers related to managing and analyzing large administrative health data sets. The methods could be applied in many other research settings to facilitate access to and analyses of information using big data.
Mining Twitter data to #educate the public about #sepsis
Authors: Guienguere Simon Olivier , Fiest Kirsten Marie , Williamson Tyler , Doig Christopher 
Published: September 2018

IntroductionSepsis is not well known. Only 58% of Americans know the word sepsis, less than 1% can identify its symptoms and one-third wrongly believe the disease is contagious (1). What if social media educated users about sepsis? There are at least 500 million tweets worldwide per day on Twitter.
Objectives and ApproachEarly detection of sepsis with early treatment is associated with a decrease in mortality (2). The current study aims to use Twitter to share sepsis patients’ experiences. The approach consisted of using text data mining techniques by randomly extracting tweets (N =150) with the hashtag #sepsissurvivor (3) using R software (4). The study retrieved and quantified sepsis patients’ tweets into word frequency distributions using documentation summarization and word cloud techniques (5) for visual representation of Twitter data. Sepsis patients used images symptoms cards (6) to raise awareness. The study used the R package "tesseract" to extract text from images (6).
ResultsPatients sharing their experiences frequently used the word “sepsis.” Cardiorespiratory compromise (septic shock—the highest mortality risk) was illustrated in the words "my heart stops” or elevated "heart" rate or “low blood pressure.” Several studies have reported increased mortality associated with delays in antibiotic administration (7). Many sepsis survivors had antibiotics exposure, both in a timely manner or delayed in use. Sepsis patients experienced long stays in the hospital. Tweets mentioned "infection" 39 times (8), which supports patients’ diagnoses in addition to high rates of "fever." The clustering technique using word association indicated infection was highly correlated with sepsis (9). Sepsis survivors shared the "pain" they went through.
Conclusion/ImplicationsTwitter presents an opportunity for patients to disseminate information about sepsis raising awareness about important symptoms. The information tweeted explores the impact of this diagnosis, and the need for early treatment. The current study demonstrates the opportunity to raise awareness through the learned experiences of patients in a novel medium.