16 research outputs found
The effects of population aging, external shocks, and job uncertainty on job performance of older workers in the Netherlands
No full textUsing Machine Learning to Understand Text for Pharmacovigilance: A Systematic Review
No full textBackground: Pharmacovigilance is a science that involves the ongoing monitoring of adverse drug reactions of existing medicines. Its primary purpose is to sustain and improve public health. The existing systems that apply the science of pharmacovigilance to practice are, however, not only expensive and time-consuming, but they also fail to include experiences from many users. The application of computational linguistics to user-generated text is hypothesized as a pro-active and an effective supplemental source of evidence. Objective: To review the existing evidence on the effectiveness of computational linguistics to understand user-generated text for the purpose of pharmacovigilance. Methodology: A broad and multi-disciplinary systematic literature search was conducted that involved four databases. Studies were considered relevant if they reported on the application of computational linguistics to understand text for pharmacovigilance. Both peer- reviewed journal articles and conference materials were included. The PRISMA guidelines were used to evaluate the quality of this systematic review. Results: A total of 16 relevant publications were included in this systematic review. All studies were evaluated to have a medium reliability and validity. Despite the quality, for all types of drugs, a vast majority of publications reported positive findings with respect to the identification of adverse drug reactions. The remaining two studies reported rather neutral results but acknowledged the potential of computational linguistics for pharmacovigilance. Conclusions: There exists consistent evidence that computational linguistics can be used effectively and accurately on user-generated textual content that was published to the Internet, to identify adverse drug reactions for the purpose of pharmacovigilance. The evidence suggests that the analysis of textual data has the potential to complement the traditional system of pharmacovigilance. Recommendations for researchers and practitioners of computational linguistics, policy makers, and users of drugs are suggested
An Explorative Study on the Perceived Challenges and Remediating Strategies for Big Data among Data Practitioners
No full textAbstract Background: Worldwide, new data are generated exponentially. The emergence of Internet of Things has resulted in products that were designed first to generate data. Big data are valuable, as they have the potential to create business value. Therefore, many organizations are now heavily investing in big data. Despite the incredible interest, big data analytics involves many challenges that need to be overcome. A taxonomy of these challenges is available that was created from the literature. However, this taxonomy fails to represent the view of data practitioners. Little is known about what practitioners do, what problems they have, and how they view the relationship between analysis and organizational innovation. Objective: The purpose of this study was twofold. First, it investigated what data practitioners consider the main challenges of big data and that may prevent creating organizational innovation. Second, it investigated what strategies these data practitioners recommend to remediate these challenges. Methodology: A survey using semi-structured interviews was performed to investigate what data practitioners view as the challenges of big data and what strategies they recommend to remediate those challenges. The study population was heterogeneous and consisted of 10 participants that were selected using purposive sampling. The interviews were conducted between February 27, 2020 and March 24, 2020. Thematic analysis was used to analyze the transcripts. Results: Ninety per cent of the data practitioners experienced working with low quality, unstructured, and incomplete data as a very time-consuming process. Various challenges related to the organizational aspects of analyzing data emerged, such as a lack of experienced human resources, insufficient knowledge of management about the process and value of big data, a lack of understanding about the role of data scientists, and issues related to communication and collaboration between employees and departments. Seventy per cent of the participants experienced insufficient time to learn new technologies and techniques. In addition, twenty per cent of practitioners experienced challenges related to accessing data, but those challenges were primarily reported by consultants. Twenty per cent argued that organizations do not use a proper data-driven approach. However, none of the practitioners experienced difficulties with data policies because this was already been taken care of by the legal department. Nevertheless, uncertainties still exist about what data can and cannot be used for analysis. The findings are only partially consistent with the taxonomy. More specifically, the reported challenges of data policies, industry structure, and access to data differ significantly. Furthermore, the challenge of data quality was not addressed in the taxonomy, but it was perceived as a major challenge to practitioners. Conclusion: The data practitioners only partially agreed with the taxonomy of challenges. The dimensions of access to data, data policies, and industry structure were not considered a challenge to creating organizational innovation. Instead, practitioners emphasized that the 3 dimension of organizational change and talent, and to a lesser extend also the dimension of technology and techniques, involve significant challenges that can severely impact the creation of organizational innovation using big data. In addition, novel and significant challenges such as data quality were identified. Furthermore, for each dimension, the practitioners recommended relevant strategies that may help others to mitigate the challenges of big data analytics and to use big data to create business value
An Explorative Study on the Perceived Challenges and Remediating Strategies for Big Data among Data Practitioners
No full textAbstract Background: Worldwide, new data are generated exponentially. The emergence of Internet of Things has resulted in products that were designed first to generate data. Big data are valuable, as they have the potential to create business value. Therefore, many organizations are now heavily investing in big data. Despite the incredible interest, big data analytics involves many challenges that need to be overcome. A taxonomy of these challenges is available that was created from the literature. However, this taxonomy fails to represent the view of data practitioners. Little is known about what practitioners do, what problems they have, and how they view the relationship between analysis and organizational innovation. Objective: The purpose of this study was twofold. First, it investigated what data practitioners consider the main challenges of big data and that may prevent creating organizational innovation. Second, it investigated what strategies these data practitioners recommend to remediate these challenges. Methodology: A survey using semi-structured interviews was performed to investigate what data practitioners view as the challenges of big data and what strategies they recommend to remediate those challenges. The study population was heterogeneous and consisted of 10 participants that were selected using purposive sampling. The interviews were conducted between February 27, 2020 and March 24, 2020. Thematic analysis was used to analyze the transcripts. Results: Ninety per cent of the data practitioners experienced working with low quality, unstructured, and incomplete data as a very time-consuming process. Various challenges related to the organizational aspects of analyzing data emerged, such as a lack of experienced human resources, insufficient knowledge of management about the process and value of big data, a lack of understanding about the role of data scientists, and issues related to communication and collaboration between employees and departments. Seventy per cent of the participants experienced insufficient time to learn new technologies and techniques. In addition, twenty per cent of practitioners experienced challenges related to accessing data, but those challenges were primarily reported by consultants. Twenty per cent argued that organizations do not use a proper data-driven approach. However, none of the practitioners experienced difficulties with data policies because this was already been taken care of by the legal department. Nevertheless, uncertainties still exist about what data can and cannot be used for analysis. The findings are only partially consistent with the taxonomy. More specifically, the reported challenges of data policies, industry structure, and access to data differ significantly. Furthermore, the challenge of data quality was not addressed in the taxonomy, but it was perceived as a major challenge to practitioners. Conclusion: The data practitioners only partially agreed with the taxonomy of challenges. The dimensions of access to data, data policies, and industry structure were not considered a challenge to creating organizational innovation. Instead, practitioners emphasized that the 3 dimension of organizational change and talent, and to a lesser extend also the dimension of technology and techniques, involve significant challenges that can severely impact the creation of organizational innovation using big data. In addition, novel and significant challenges such as data quality were identified. Furthermore, for each dimension, the practitioners recommended relevant strategies that may help others to mitigate the challenges of big data analytics and to use big data to create business value
Surveillance of communicable diseases using social media: A systematic review
No full textBackground Communicable diseases pose a severe threat to public health and economic growth. The traditional methods that are used for public health surveillance, however, involve many drawbacks, such as being labor intensive to operate and resulting in a lag between data collection and reporting. To effectively address the limitations of these traditional methods and to mitigate the adverse effects of these diseases, a proactive and real-time public health surveillance system is needed. Previous studies have indicated the usefulness of performing text mining on social media. Objective To conduct a systematic review of the literature that used textual content published to social media for the purpose of the surveillance and prediction of communicable diseases.MethodologyBroad search queries were formulated and performed in four databases. Both journal articles and conference materials were included. The quality of the studies, operationalized as reliability and validity, was assessed. This qualitative systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results Twenty-three publications were included in this systematic review. All studies reported positive results for using textual social media content to surveille communicable diseases. Most studies used Twitter as a source for these data. Influenza was studied most frequently, while other communicable diseases received far less attention. Journal articles had a higher quality (reliability and validity) than conference papers. However, studies often failed to provide important information about procedures and implementation. Conclusion Text mining of health-related content published on social media can serve as a novel and powerful tool for the automated, real-time, and remote monitoring of public health and for the surveillance and prediction of communicable diseases in particular. This tool can address limitations related to traditional surveillance methods, and it has the potential to supplement traditional methods for public health surveillance.Validerad;2023;Nivå 2;2023-03-07 (joosat);Licens fulltext: CC BY License</p
Using Machine Learning for Pharmacovigilance: A Systematic Review
No full textPharmacovigilance is a science that involves the ongoing monitoring of adverse drug reactions to existing medicines. Traditional approaches in this field can be expensive and time-consuming. The application of natural language processing (NLP) to analyze user-generated content is hypothesized as an effective supplemental source of evidence. In this systematic review, a broad and multi-disciplinary literature search was conducted involving four databases. A total of 5318 publications were initially found. Studies were considered relevant if they reported on the application of NLP to understand user-generated text for pharmacovigilance. A total of 16 relevant publications were included in this systematic review. All studies were evaluated to have medium reliability and validity. For all types of drugs, 14 publications reported positive findings with respect to the identification of adverse drug reactions, providing consistent evidence that natural language processing can be used effectively and accurately on user-generated textual content that was published to the Internet to identify adverse drug reactions for the purpose of pharmacovigilance. The evidence presented in this review suggest that the analysis of textual data has the potential to complement the traditional system of pharmacovigilance
A Longitudinal Analysis of Job Satisfaction During a Recession in the Netherlands
No full textBetween 2008 and 2013, the Netherlands was confronted by a severe recession. This recession may have affected the job satisfaction of workers. Currently, little is known about how job satisfaction changes during a recession. To investigate the effect of the 2008-2013 recession on job satisfaction in the Netherlands, and to assess how job satisfaction changed over time. Longitudinal data from six waves of a national panel in the Netherlands are used to investigate the effects. These data capture the periods before, during and after the recession. A Blinder-Oaxaca decomposition technique is used to decompose the ordinal outcome variable job satisfaction. Subsequent waves are compared, which results in five comparison groups. Workers who participated in subsequent waves are matched to assess their job satisfaction over time. Cross-sectional associations are analyzed using the entire unmatched dataset. Workers became more satisfied with their job during the recession. After the recession ended, average job satisfaction decreased again. Both unmatched and matched analyses indicated only changes in job level affecting job satisfaction. The coefficient of education had a small effect cross-sectionally. The level of education and industry had a small effect longitudinally. However, these effects were not robust. Job satisfaction decreased before the recession commenced but increased during the recession. After the recession, job satisfaction decreased again. An increase in job satisfaction during the recession may be explained by a change in the composition of workers with respect to job level, instead of by the effect of predictors.</p
Description of studies analyzed by social media platform (23 studies included).
No full textDescription of studies analyzed by social media platform (23 studies included).</p
The Effect of an Increase of the Retirement Age on the Health, Well-Being, and Labor Force Participation of Older Workers:a Systematic Literature Review
No full textTo sustain a viable public pension system, many governments have increased the statutory retirement age and delayed the age of entitlement to public pension benefits. This systematic literature review investigates the empirical evidence on the effects of increasing the retirement age on the health, well-being, and labor force participation of older workers. Optimized and broad search queries were used to search for empirical evidence in four databases: EconLit, PsycINFO, PubMed, and SocINDEX. The systematic literature search was conducted in May 2019. Snowballing was performed on the reference lists of the publications to find additional studies. The quality of the included studies was also examined. The PRISMA guidelines were used to guide this systematic literature review. Nineteen studies were included in this review. Twelve studies estimated the effect of an increase in the statutory retirement age, and seven studies examined working beyond the retirement age. The reported findings were classified into health-related outcomes, well-being, and the effects on labor force participation and the perception of the retirement age. The reported findings regarding health-related outcomes and well-being were not comparable. The increase of the retirement age has increased labor force participation among older workers and has increased the preferred and expected retirement age in the direction of the public pension reform. However, evidence on the effects of an increase of the retirement age on the health and well-being of older workers remains scarce and inconclusive
Results of study selection.
No full textBackgroundCommunicable diseases pose a severe threat to public health and economic growth. The traditional methods that are used for public health surveillance, however, involve many drawbacks, such as being labor intensive to operate and resulting in a lag between data collection and reporting. To effectively address the limitations of these traditional methods and to mitigate the adverse effects of these diseases, a proactive and real-time public health surveillance system is needed. Previous studies have indicated the usefulness of performing text mining on social media.ObjectiveTo conduct a systematic review of the literature that used textual content published to social media for the purpose of the surveillance and prediction of communicable diseases.MethodologyBroad search queries were formulated and performed in four databases. Both journal articles and conference materials were included. The quality of the studies, operationalized as reliability and validity, was assessed. This qualitative systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.ResultsTwenty-three publications were included in this systematic review. All studies reported positive results for using textual social media content to surveille communicable diseases. Most studies used Twitter as a source for these data. Influenza was studied most frequently, while other communicable diseases received far less attention. Journal articles had a higher quality (reliability and validity) than conference papers. However, studies often failed to provide important information about procedures and implementation.ConclusionText mining of health-related content published on social media can serve as a novel and powerful tool for the automated, real-time, and remote monitoring of public health and for the surveillance and prediction of communicable diseases in particular. This tool can address limitations related to traditional surveillance methods, and it has the potential to supplement traditional methods for public health surveillance.</div
