While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what (...) are experimental artifacts and what is the most productive way of dealing with them? In this paper, I approach these questions by exploring the ways in which experimenters in psychology simultaneously exploit and suppress the reactivity of their subject matter in order to produce experimental data that speak to the question or subject matter at hand. Highlighting the artificiality of experimental data, I raise the question of what distinguishes a genuine experimental result from an experimental artifact. My analysis construes experimental results as the outcomes of inferences from the data that take material background assumptions as auxiliary premises. Artifacts occur when one or more of these background assumptions are false, such that the data do not reliably serve the purposes they were generated for. I conclude by laying out the ways in which my analysis of data quality is relevant to, and informed by, recent debates about the replicability of experimental results. (shrink)

In this commentary, I propose a framework for thinking about data quality in the context of scientific research. I start by analyzing conceptualizations of quality as a property of information, evidence and data and reviewing research in the philosophy of information, the philosophy of science and the philosophy of biomedicine. I identify a push for purpose dependency as one of the main results of this review. On this basis, I present a contextual approach to data (...) quality in scientific research, whereby the quality of a dataset is dependent on the context of use of the dataset as much as the dataset itself. I exemplify the approach by discussing current critiques and debates of scientific quality, thus showcasing how data quality can be approached contextually. (shrink)

Internet recruitment methods for research are rapidly evolving as technology and participant preferences do as well. This brings data security concerns, balanced with respect to persons for research participants. Internet recruitment research strategies are still important given the importance of creating private and accessible pathways for potentially marginalized populations or people experiencing stigmatized mental health conditions to participate in research. This manuscript describes the case of social media recruitment for a mental health and racism study in Fall 2022 that (...) was infiltrated by bots. We contextualize it in the context of data quality literature, with considerations for future directions including strategies for study design and management, and further research questions. (shrink)

Wearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, wearables have also been associated with issues and risks, such as those connected to privacy and data sharing. Yet, discussions in the literature have mostly focused on either technical or ethical considerations, framing these as largely separate areas of discussion, and the contribution of (...) wearables to the collection, development, application of biomedical knowledge has only partially been discussed. To fill in these gaps, in this article we provide an epistemic (knowledgerelated) overview of the main functions of wearable technology for health: monitoring, screening, detection, and prediction. On this basis, we identify 4 areas of concern in the application of wearables for these functions: data quality, balanced estimations, health equity, and fairness. To move the field forward in an effective and beneficial direction, we present recommendations for the 4 areas: local standards of quality, interoperability, access, and representativity. (shrink)

The research is focused on the role of two related key concepts, namely variables and data, in the impact evaluations of public projects. A difficult task of the evaluators and researchers is to select the appropriate variables to ensure the best model of reality and satisfy the evaluation methods' needs. Therefore, the paper aims to look at the current knowledge and discuss how variables and data could be best used to connect the evaluation models, the particularities of the (...) intervention with the potential of the advanced quantitative assessment methods. The results emphasise that evaluations operate with data with different levels of granularity, as required by the intervention logic. Structuring data in clusters and categories, performing evaluability assessments are useful in assessing data quality and limitations and improving them. In line with the existing literature, we demonstrate that data accessibility is a key constraint and imposes adjustment of the desired evaluation model to a feasible one. While Big Data and Open Data systems significantly improved data quality in evaluations in recent years, blockchain, as a ledger technology with default features related to decentralisation and security, is expected to bring large benefits to evaluation. For evaluators and policymakers, blockchain potential is an area of further research looking for additional advantages that could enhance the use of quantitative methods. (shrink)

Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social (...) and psychological backgrounds of individuals correlate in practice with different modes of human–computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia. (shrink)

This paper provides an in-depth analysis and discussion of the key issues of quality management, evaluation, and detection contained in big data for automotive application scenarios. A generalized big data quality management model and programming framework are proposed, and a series of data quality detection and repair interfaces are built to express the processing semantics of various data quality issues. Through this data quality management model and detection and repair interfaces, (...) users can quickly build custom data quality detection and repair tasks for different data quality requirements. To improve the operational efficiency of complex data quality management algorithms in large-scale data scenarios, corresponding parallelization algorithms are studied and implemented for detection and repair algorithms with long computation time, including priority-based multiconditional function-dependent detection and repair algorithms, entity detection, and extraction algorithms based on semantic information and chunking techniques, and plain Bayesian-based missing value filling algorithms, and this paper proposes a data validity evaluation algorithm and enhances the validity of the original data in practical applications by adding temporal weights, and finally it passed the experimental validation. Through the comprehensive detection process of data importance, network busyness, duration of transmission process, and failure situation, the efficiency has been increased by 20%, and an adaptive data integrity detection method based on random algorithm and encryption algorithm is designed. After experimental verification, this method can effectively detect the integrity of the data transmission process and improve the application of data value, and the final effect is increased by 30.5%. (shrink)

Whether due to simplicity or hypocrisy, the question of access to patient data for biomedical research is widely seen in the public discourse only from the angle of patient privacy. At the same time, the desire to live and to live without disability is of much higher value to the patients. This goal can only be achieved by extracting research insight from patient data in addition to working on model organisms, something that is well understood by many patients. (...) Yet, most biomedical researchers working outside of clinics and hospitals are denied access to patient records when, at the same time, clinicians who guard the patient data are not optimally prepared for the data’s analysis. Medical data collection is a time- and cost-intensive process that is most of all tedious, with few elements of intellectual and emotional satisfaction on its own. In this process, clinicians and bioinformaticians, each group with their own interests, have to join forces with the goal to generate medical data sets both from clinical trials and from routinely collected electronic health records that are, as much as possible, free from errors and obvious inconsistencies. The data cleansing effort as we have learned during curation of Singaporean clinical trial data is not a trivial task. The introduction of omics and sophisticated imaging modalities into clinical practice that are only partially interpreted in terms of diagnosis and therapy with today’s level of knowledge warrant the creation of clinical databases with full patient history. This opens up opportunities for re-analyses and cross-trial studies at future time points with more sophisticated analyses of the same data, the collection of which is very expensive. (shrink)

Objective Some researchers claim that the quality of informed consent of clinical research participants in developing countries is worse than in developed countries. To evaluate this assumption, we reviewed the available data on the quality of consent in both settings. Methods We conducted a comprehensive PubMed search, examined bibliographies and literature reviews, and consulted with international experts on informed consent in order to identify studies published from 1966 to 2010 that used quantitative methods, surveyed participants or parents (...) of paediatric participants in actual trials, assessed comprehension and/or voluntariness, and did not involve testing particular consent interventions. Forty-seven studies met these criteria. We compared data about participant comprehension and voluntariness. The paucity of data and variation in study methodology limit comparison and preclude statistical aggregation of the data. Results and Discussion This review shows that the assertion that informed consent is worse in developing countries than in developed countries is a simplification of a complex picture. Despite the limitations of comparison, the data suggest that: (1) comprehension of study information varies among participants in both developed and developing countries, and comprehension of randomisation and placebo controlled designs is poorer than comprehension of other aspects of trials in both settings; and (2) participants in developing countries appear to be less likely than those in developed countries to say they can refuse participation in or withdraw from a trial, and are more likely to worry about the consequences of refusal or withdrawal. (shrink)

Fake news has become an important topic of research in a variety of disciplines including linguistics and computer science. In this paper, we explain how the problem is approached from the perspective of natural language processing, with the goal of building a system to automatically detect misinformation in news. The main challenge in this line of research is collecting quality data, i.e., instances of fake and real news articles on a balanced distribution of topics. We review available datasets (...) and introduce the MisInfoText repository as a contribution of our lab to the community. We make available the full text of the news articles, together with veracity labels previously assigned based on manual assessment of the articles’ truth content. We also perform a topic modelling experiment to elaborate on the gaps and sources of imbalance in currently available datasets to guide future efforts. We appeal to the community to collect more data and to make it available for research purposes. (shrink)

High vocation education is an important foundation for China to cultivate high teaching quality and technical and skilled talents. In the new era, the acceleration of the development of modern vocational education is put in a more prominent position. It is proposed that we should adhere to moral education, closely combine this with the needs of technological change and industrial upgrading, constantly improve the quality of high vocational education teaching, and cultivate more technical and skilled talents with both (...) political integrity and ability for modernization construction. Under the background of social informatization, using artificial intelligent technology to solve these problems can play an important role for improving the teaching quality of high vocational education. This paper proposed a data mining approach in promoting student satisfaction with the teaching quality of high vocational education. We design a questionnaire for Students’ satisfaction with the teaching quality of basic entrepreneurship curriculum. We take the survey data of vocation education as an example and use mining technology analysis software to understand the current status of the teaching quality of basic entrepreneurship curriculum. The results determine the main factors affecting Students’ satisfaction with teaching quality. The results of this paper can be used in student management, education strategy, student education satisfaction, and teaching quality in high vocation college education, and to improve the teaching quality of fundamentals of entrepreneurship curriculum in high vocation education. (shrink)

The use of sensors has had an enormous increment in the last years, becoming a valuable tool in many different areas. In this kind of scenario, the quality of data becomes an extremely important issue; however, not much attention has been paid to this specific topic, with only a few existing works that focus on it. In this paper, we present a proposal for managing data streams from sensors that are installed in patients’ homes in order to (...) monitor their health. It focuses on processing the sensors’ data streams, taking into account data quality. In order to achieve this, a data quality model for this kind of data streams and an architecture for the monitoring system are proposed. Moreover, our work introduces a mechanism for avoiding false alarms generated by data quality problems. (shrink)

In this opening chapter, we review the literature on information quality. Our major aim is to introduce the issues, and trace some of the history of the debates, with a view to situating the chapters in this volume – whose authors come from different disciplines – to help make them accessible to readers with different backgrounds and expertise. We begin in this section by tracing some influential analyses of IQ in computer science. This is a useful basis for examining (...) some examples of developing work on IQ in section two. We look at some cases for applying IQ in section three, and conclude with some discussion points in section four. (shrink)

This paper is divided into two parts. In the first, I shall briefly analyse the phenomenon of “big data”, and argue that the real epistemological challenge posed by the zettabyte era is small patterns. The valuable undercurrents in the ocean of data that we are accumulating are invisible to the computationally-naked eye, so more and better technology will help. However, because the problem with big data is small patterns, ultimately, the game will be won by those who (...) “know how to ask and answer questions” (Plato, Cratylus, 390c). This introduces the second part, concerning information quality (IQ): which data may be useful and relevant, and so worth collecting, curating, and querying, in order to exploit their valuable (small) patterns? I shall argue that the standard way of seeing IQ in terms of fit-for-purpose is correct but needs to be complemented by a methodology of abstraction, which allows IQ to be indexed to different purposes. This fundamental step can be taken by adopting a bi-categorical approach. This means distinguishing between purpose/s for which some information is produced (P-purpose) and purpose/s for which the same information is consumed (C-purpose). Such a bi-categorical approach in turn allows one to analyse a variety of so-called IQ dimensions, such as accuracy, completeness, consistency, and timeliness. I shall show that the bi-categorical approach lends itself to simple visualisations in terms of radar charts. (shrink)

A novel data-driven model-free adaptive control approach is first proposed by combining the advantages of model-free adaptive control and data-driven optimal iterative learning control, and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. Besides, the parameters of presented approach are adaptively adjusted with fuzzy logic to determine the occupied proportions of MFAC and DDOILC according to their different control performances in different control stages. Lastly, the proposed fuzzy DDMFAC (...) approach is applied to the control of particle quality in drug development phase of spray fluidized-bed granulation process, and its control effect is compared with MFAC and DDOILC and their fuzzy forms, in which the parameters of MFAC and DDOILC are adaptively adjusted with fuzzy logic. The effectiveness of the presented FDDMFAC approach is verified by a series of simulations. (shrink)

A course focusing on ethical issues in physics has been taught to undergraduate students at Eastern Michigan University since 1988. The course covers both responsible conduct of research and ethical issues associated with how physicists interact with the rest of society. Since most undergraduate physics majors will not have a career in academia, it is important that a course such as this address issues that will be relevant to physicists in a wide range of job situations. There is a wealth (...) of published work that can be drawn on for reading assignments. (shrink)

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing (...) objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs. (shrink)

A major methodological problem in victimization surveys on physical and sexual violence against women is the underreporting of violence. The first part of this article makes a case for 6 feminist strategies for improving the accuracy of self-report data on victimization within a mainstream survey research framework. The second part of the article is a presentation of data from a survey of Toronto women that is designed to show the efficacy of these feminist strategies.

Ozone is one of the pollutants with most negative effects on human health and in general on the biosphere. Many data-acquisition networks collect data about ozone values in both urban and background areas. Usually, these data are incomplete or corrupt and the imputation of the missing values is a priority in order to obtain complete datasets, solving the uncertainty and vagueness of existing problems to manage complexity. In the present paper, multiple-regression techniques and Artificial Neural Network models (...) are applied to approximate the absent ozone values from five explanatory variables containing air-quality information. To compare the different imputation methods, real-life data from six data-acquisition stations from the region of Castilla y León are gathered in different ways and then analyzed. The results obtained in the estimation of the missing values by applying these techniques and models are compared, analyzing the possible causes of the given response. (shrink)

Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis’s (2012) statement that, “the scientific process demands the highest standards of quality, ethics and rigour,” they are worth implementing. More importantly, they are feasible to (...) work on and likely will help to restore plausibility to social sciences research. Therefore, I feel it likely that the triplet of open data, open review and open dialogue will gradually emerge to become policy requirements regardless of the research funding source. (shrink)

This guide accompanies the following article(s): ‘Full Disclosure of the “Raw Data” of Research on Humans: Citizens’ Rights, Product Manufacturer’s Obligations and the Quality of the Scientific Database.’Philosophy Compass 6/2 (2011): 90–99. doi: 10.1111/j.1747‐9991.2010.00376.x Author’s Introduction Securing consent (and informed consent) from patients and research study participants is a key concern in patient care and research on humans. Yet, the legal doctrines of consent and informed consent differ in their applications. In patient care, the judicial doctrines of consent (...) and informed consent are disclosure doctrines based on the obligation of physicians to inform their patients of the following types of information: the nature of the procedure the physician is recommending in the patent’s care (P), the alternatives to the physician‐recommendation (A), and the risks of procedure and its alternatives (R). In addition, the physician must provide truthful answers to the patient’s questions (Q) to the best of the physician’s abilities. In research on humans, the onus of disclosure by study sponsors and principal investigators is much greater than in patient care precisely because the purpose of research is not patient care. The purpose of research is the identification and development of new generalizable knowledge. Thus, participation in a research study or trial may not benefit the study volunteer at all. In addition, the participant may carry a heavy risk burden in relationship to any testing of a newly designed drug or device as part of their experiences in the research study. Presently, despite the risks borne by the research study participant, the raw data collected from volunteers in research trials are treated by courts as if they were the private property of the study sponsor rather than generally owed to the public as would be expected by an effort like research participation aimed at contributing to the development of generalizable knowledge. This paper reviews the obligations that study sponsors owe to their study participants based on the very definition of research as a systematic activity whose purpose is to develop generalizable knowledge to help all humans. Author Recommends Court cases Canterbury v. Spence, United States Court of Appeals, District of Columbia Circuit 464 F.2d 772 (1972). (Federal appellate decision heard in the District of Columbia, USA) Reibl v. Hughes, 2 S.C.R. 880 (1980). (Supreme Court of Canada) Rogers v. Whitaker, 175 C.L. R. 479 (1992). (High Court of Australia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Canterbury v. Spence is the landmark Federal informed consent case in the United States heard in the District of Columbia. In this case, Judge Spottswood Robinson articulated the reasonable person standard of informed consent. This standard was subsequently adopted by the Supreme Court of Canada (Reibl v. Hughes, 2 S.C.R. 880 (1980)) and the High Court of Australia (Rogers v. Whitaker, 175 C.L. R. 479 (1992)). The House of Lords rejected the reasonable person standard in England in Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985). Book Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. This book is a detailed guide for the review of scientific and ethical aspects of research on humans contained in the research study protocols and research informed consent forms which need to be submitted by study sponsors and principal investigators to institutional review boards (IRBs) for IRB review before a research study can receive approval to be conducted within a human study population in those institutions over which the IRB has authority. It also presents basic definitions in the area of research on humans and basic approaches to help conduct a deep review of the underlying scientific and ethical issues that are present within a research study protocol and its accompanying research informed consent form. Report National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Guidelines for the Protection of Human Subjects of Research. Washington, DC: DHEW Publications, 1978 (78‐0012). This U.S. National Commission in the Belmont Report argued for the necessity of a reasonable volunteer standard in research on humans in the United States. Arguing that the professional standard and the reasonable person standard of consent and informed consent were insufficient to base a disclosure standard in research on humans, this National Commission developed the reasonable volunteer standard where a study participant is to be approached in research informed consent with that information that a reasonable volunteer would want to know. Online Materials Belmont Report: Syllabus Topics for Lecture and Discussion Week I: Introduction and Overview Readings: Rogers v. Whitaker, 175 C.L.R. 479 (1992). (High Court of Australia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Week II: The Different Standards of Consent and Informed Consent Readings: Canterbury v. Spence, 464 F.2d 772 (1972). (Federal appellate opinion heard in the District of Columbia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Week III: Definition of Research Reading: Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. (Especially Chapters 2 and 3) Week IV: How to Review a Research Study Protocol and Research Informed Consent Form Reading: Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. (Especially Chapters 4–6, 10, and 12–14) Week V: Obligations in Research of Humans Versus Patient Care Reading: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Guidelines for the Protection of Human Subjects of Research. Washington, DC: DHEW Publications, 1978 (78‐0012). Focus Questions • How do the judicial doctrines of consent in England and Australia compare to the judicial doctrines of informed consent in the United States and Canada? • How does a professional standard of disclosure differ from a reasonable person standard of disclosure? • What is the definition of research? • How is a research study (study proposal and research informed consent form) reviewed from scientific and ethical bases for considerations of approval by an IRB? • How do the obligations of a study sponsor, a principal investigator, and a research team conducting a research trial on human study participants differ from the obligations of a physician caring for a patient? • Should all raw data of research derived from humans be made available to the public for use in developing generalizable knowledge to help others, or should raw data be considered the private property of the study sponsors (for example, prescription drug manufacturers), who fund the research trials? Seminar/project Idea Draw a timeline of the development of the basic concepts of consent and informed consent in patient care and in patient research. On this timeline, first place the dates of the key court cases in England, the United States, Canada, and Australia on consent and informed consent. Then place the date of the development of the Belmont Report. Why do differences exist between these four countries in terms of the legal requirements of informedness of a patient (in medical care) and a study participant (in medical research)? What roles if any do the concepts of self‐determination and self‐decision have in consent and informed consent in patient care and in informed consent in research on humans among the four countries of interest? What are the realities of research on human participants that require a new standard of informed consent, the reasonable volunteer standard, to be developed beyond the two standards in patient care, the professional standard, and the reasonable person standard? Counterpoint Arguments The following are counterpoint arguments that need to be appreciated to more fully understand the concepts that impact the thesis that ‘raw data’ should be open to the public to allow the opportunity of checking of the results of its associated scientific paper and for further analysis. De‐Identified (Anonymized) Raw Data A current approach to identifying (anonymizinig) raw data is based on the Health Insurance Portability and Accountability Act (HIPAA) of 1996 (P.L.104‐191). HIPAA was enacted by the U.S. Congress in 1996 to keep a person’s medical information private. In a research study, a person’s private medical information can become research data. HIPAA’s Privacy Rule allows a covered entity to de‐identify data by removing all 18 elements that could be used to identify the individual or the individual’s relatives, employers, or household members; these elements are enumerated in the Privacy Rule. The covered entity must also have no actual knowledge that the remaining information could be used alone or in combination with other information to identify the individual who is the subject of the information. Under this method, the identifiers that must be removed are the following: 1 Names. 2 All geographic subdivisions smaller than a state, including street address, city, county, precinct, ZIP Code, and their equivalent geographical codes, except for the initial three digits of a ZIP Code if, according to the current publicly available data from the Bureau of the Census: 2a. The geographic unit formed by combining all ZIP Codes with the same three initial digits contains more than 20,000 people. 2b. The initial three digits of a ZIP Code for all such geographic units containing 20,000 or fewer people are changed to 000. 3 All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, and date of death, and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older. 4 Telephone numbers. 5 Facsimile numbers. 6 Electronic mail addresses. 7 Social security numbers. 8 Medical record numbers. 9 Health plan beneficiary numbers. 10 Account numbers. 11 Certificate/license numbers. 12 Vehicle identifiers and serial numbers, including license plate numbers. 13 Device identifiers and serial numbers. 14 Web universal resource locators (URLs). 15 Internet protocol (IP) address numbers. 16 Biometric identifiers, including fingerprints and voiceprints. 17 Full‐face photographic images and any comparable images. 18 Any other unique identifying number, characteristic, or code, unless otherwise permitted by the Privacy Rule for re‐identification. Even though ‘raw data’ can be de‐identified (anonymized) so that there is no way to trace back the data to the study participant from which it was drawn, there are still questions whether the ‘de‐identified raw data’ can be given to other researchers or made public without the original study participant’s approval. It may be the case that this approval could be done at the start of any research study where the individual considering study participation would be counseled on what the process of de‐identification consists of and would be asked whether the participant would allow use of the data by other researchers or made available to the public in a de‐identified state. However, there may be questions of the study participants as to whether the data are truly de‐identified and untraceable back to the study participant since the participant would not be able to see the process actually being carried out in detail in all relevant circumstances. Using Raw Data in Context Simply being in possession of raw data is never enough to build on that data from a research perspective. Researchers need to understand the context of the scientific research study in which the data were collected. This research context includes (but is not limited to): (i) the scientific hypothesis of the research study, (ii) the inclusion and exclusion criteria selected by the research designers of the study to determine which study participants were included and which were excluded from the study, (iii) the methods used to collect the data and possibilities of errors in the data collection processes, and (iv) answers to questions regarding whether any data were excluded from the study data and why these data were excluded. Ownership Rights Over Data There are ownership rights over data. Ownership rights include court decision making related to the question which parties are to be considered as the owners of the data. Possible owners here include (but are not limited to) (i) the study sponsor whose only role was to fund that study, (ii) the principal investigator(s) who developed the scientific hypothesis, and (iii) individual study participants through special contracts made by individual participants who negotiate such contracts with study sponsors and principal investigators to share in the ownership of data. Examples of such data are participant sources of unique gene lines or unique cell lines to be used in future research. Objections to Data Sharing Finally, individual researchers may simply object to sharing data. A researcher’s arguments here may be simply stating that he or she has a right to fully analyze the data since the collection of the data was the researchers’ idea in the first place. These researchers may argue that they have the right to mine the data as fully as they can before deciding to make the data open to the public for others to analyze. Works Cited 1 Kaiser, J. ‘Making Clinical Data Widely Available.’Science 322.5899 (10 Oct. 2008): 217–8. 2 de Lusignan, S., J.F. Metsemakers, P. Houwink, V. Gunnarsdottir, and J. van der Lei. ‘Routinely Collected General Practice Data: Goldmines for Research? A Report of the European Federation for Medical Informatics Primary Care Informatics Working Group (EFMI PCIWG) from MIE2006, Maastricht, The Netherlands.’Informatics in Primary Care 14.3 (2006): 203–9. 3 Sim, I. ‘Human Studies Database Project’, CTSA Data Repositories Symposium (17 Oct. 2008). 23 Nov. 2010. 4 Sim, I., C.G. Chute, H. Lehmann, R. Nagarajan, M. Nahm, and R.H. Scheuermann. ‘Keeping Raw Data in Context.’Science 323.5915 (6 Feb. 2009): 713. 5 U.S. Department of Health and Human Services, National Institutes of Health, HIPAA Privacy Rule, Information for Researchers. 13 Nov. 2010. 6 Wilczynski, N.L., R.B. Haynes, and Hedges Team. ‘EMBASE Search Strategies for Identifying Methodologically Sound Diagnostic Studies for Use by Clinicians and Researchers.’BMC Medicine 29.3 (March 2005): 7. (shrink)

This guide accompanies the following article(s): ‘Full Disclosure of the “Raw Data” of Research on Humans: Citizens’ Rights, Product Manufacturer’s Obligations and the Quality of the Scientific Database.’Philosophy Compass 6/2 (2011): 90–99. doi: 10.1111/j.1747‐9991.2010.00376.x Author’s Introduction Securing consent (and informed consent) from patients and research study participants is a key concern in patient care and research on humans. Yet, the legal doctrines of consent and informed consent differ in their applications. In patient care, the judicial doctrines of consent (...) and informed consent are disclosure doctrines based on the obligation of physicians to inform their patients of the following types of information: the nature of the procedure the physician is recommending in the patent’s care (P), the alternatives to the physician‐recommendation (A), and the risks of procedure and its alternatives (R). In addition, the physician must provide truthful answers to the patient’s questions (Q) to the best of the physician’s abilities. In research on humans, the onus of disclosure by study sponsors and principal investigators is much greater than in patient care precisely because the purpose of research is not patient care. The purpose of research is the identification and development of new generalizable knowledge. Thus, participation in a research study or trial may not benefit the study volunteer at all. In addition, the participant may carry a heavy risk burden in relationship to any testing of a newly designed drug or device as part of their experiences in the research study. Presently, despite the risks borne by the research study participant, the raw data collected from volunteers in research trials are treated by courts as if they were the private property of the study sponsor rather than generally owed to the public as would be expected by an effort like research participation aimed at contributing to the development of generalizable knowledge. This paper reviews the obligations that study sponsors owe to their study participants based on the very definition of research as a systematic activity whose purpose is to develop generalizable knowledge to help all humans. Author Recommends Court cases Canterbury v. Spence, United States Court of Appeals, District of Columbia Circuit 464 F.2d 772 (1972). (Federal appellate decision heard in the District of Columbia, USA) Reibl v. Hughes, 2 S.C.R. 880 (1980). (Supreme Court of Canada) Rogers v. Whitaker, 175 C.L. R. 479 (1992). (High Court of Australia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Canterbury v. Spence is the landmark Federal informed consent case in the United States heard in the District of Columbia. In this case, Judge Spottswood Robinson articulated the reasonable person standard of informed consent. This standard was subsequently adopted by the Supreme Court of Canada (Reibl v. Hughes, 2 S.C.R. 880 (1980)) and the High Court of Australia (Rogers v. Whitaker, 175 C.L. R. 479 (1992)). The House of Lords rejected the reasonable person standard in England in Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985). Book Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. This book is a detailed guide for the review of scientific and ethical aspects of research on humans contained in the research study protocols and research informed consent forms which need to be submitted by study sponsors and principal investigators to institutional review boards (IRBs) for IRB review before a research study can receive approval to be conducted within a human study population in those institutions over which the IRB has authority. It also presents basic definitions in the area of research on humans and basic approaches to help conduct a deep review of the underlying scientific and ethical issues that are present within a research study protocol and its accompanying research informed consent form. Report National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Guidelines for the Protection of Human Subjects of Research. Washington, DC: DHEW Publications, 1978 (78‐0012). This U.S. National Commission in the Belmont Report argued for the necessity of a reasonable volunteer standard in research on humans in the United States. Arguing that the professional standard and the reasonable person standard of consent and informed consent were insufficient to base a disclosure standard in research on humans, this National Commission developed the reasonable volunteer standard where a study participant is to be approached in research informed consent with that information that a reasonable volunteer would want to know. Online Materials Belmont Report: Syllabus Topics for Lecture and Discussion Week I: Introduction and Overview Readings: Rogers v. Whitaker, 175 C.L.R. 479 (1992). (High Court of Australia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Week II: The Different Standards of Consent and Informed Consent Readings: Canterbury v. Spence, 464 F.2d 772 (1972). (Federal appellate opinion heard in the District of Columbia) Sidaway v. Board of Governors of the Bethlem Royal Hospital and the Maudsley Hospital and Others. 1 A.C. 871 (1985) (House of Lords) Week III: Definition of Research Reading: Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. (Especially Chapters 2 and 3) Week IV: How to Review a Research Study Protocol and Research Informed Consent Form Reading: Mazur, Dennis J. The Science and Ethics of Research on Humans. Baltimore, MD: Johns Hopkins University Press, 2007. (Especially Chapters 4–6, 10, and 12–14) Week V: Obligations in Research of Humans Versus Patient Care Reading: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: Ethical Guidelines for the Protection of Human Subjects of Research. Washington, DC: DHEW Publications, 1978 (78‐0012). Focus Questions • How do the judicial doctrines of consent in England and Australia compare to the judicial doctrines of informed consent in the United States and Canada? • How does a professional standard of disclosure differ from a reasonable person standard of disclosure? • What is the definition of research? • How is a research study (study proposal and research informed consent form) reviewed from scientific and ethical bases for considerations of approval by an IRB? • How do the obligations of a study sponsor, a principal investigator, and a research team conducting a research trial on human study participants differ from the obligations of a physician caring for a patient? • Should all raw data of research derived from humans be made available to the public for use in developing generalizable knowledge to help others, or should raw data be considered the private property of the study sponsors (for example, prescription drug manufacturers), who fund the research trials? Seminar/project Idea Draw a timeline of the development of the basic concepts of consent and informed consent in patient care and in patient research. On this timeline, first place the dates of the key court cases in England, the United States, Canada, and Australia on consent and informed consent. Then place the date of the development of the Belmont Report. Why do differences exist between these four countries in terms of the legal requirements of informedness of a patient (in medical care) and a study participant (in medical research)? What roles if any do the concepts of self‐determination and self‐decision have in consent and informed consent in patient care and in informed consent in research on humans among the four countries of interest? What are the realities of research on human participants that require a new standard of informed consent, the reasonable volunteer standard, to be developed beyond the two standards in patient care, the professional standard, and the reasonable person standard? Counterpoint Arguments The following are counterpoint arguments that need to be appreciated to more fully understand the concepts that impact the thesis that ‘raw data’ should be open to the public to allow the opportunity of checking of the results of its associated scientific paper and for further analysis. De‐Identified (Anonymized) Raw Data A current approach to identifying (anonymizinig) raw data is based on the Health Insurance Portability and Accountability Act (HIPAA) of 1996 (P.L.104‐191). HIPAA was enacted by the U.S. Congress in 1996 to keep a person’s medical information private. In a research study, a person’s private medical information can become research data. HIPAA’s Privacy Rule allows a covered entity to de‐identify data by removing all 18 elements that could be used to identify the individual or the individual’s relatives, employers, or household members; these elements are enumerated in the Privacy Rule. The covered entity must also have no actual knowledge that the remaining information could be used alone or in combination with other information to identify the individual who is the subject of the information. Under this method, the identifiers that must be removed are the following: 1 Names. 2 All geographic subdivisions smaller than a state, including street address, city, county, precinct, ZIP Code, and their equivalent geographical codes, except for the initial three digits of a ZIP Code if, according to the current publicly available data from the Bureau of the Census: 2a. The geographic unit formed by combining all ZIP Codes with the same three initial digits contains more than 20,000 people. 2b. The initial three digits of a ZIP Code for all such geographic units containing 20,000 or fewer people are changed to 000. 3 All elements of dates (except year) for dates directly related to an individual, including birth date, admission date, discharge date, and date of death, and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older. 4 Telephone numbers. 5 Facsimile numbers. 6 Electronic mail addresses. 7 Social security numbers. 8 Medical record numbers. 9 Health plan beneficiary numbers. 10 Account numbers. 11 Certificate/license numbers. 12 Vehicle identifiers and serial numbers, including license plate numbers. 13 Device identifiers and serial numbers. 14 Web universal resource locators (URLs). 15 Internet protocol (IP) address numbers. 16 Biometric identifiers, including fingerprints and voiceprints. 17 Full‐face photographic images and any comparable images. 18 Any other unique identifying number, characteristic, or code, unless otherwise permitted by the Privacy Rule for re‐identification. Even though ‘raw data’ can be de‐identified (anonymized) so that there is no way to trace back the data to the study participant from which it was drawn, there are still questions whether the ‘de‐identified raw data’ can be given to other researchers or made public without the original study participant’s approval. It may be the case that this approval could be done at the start of any research study where the individual considering study participation would be counseled on what the process of de‐identification consists of and would be asked whether the participant would allow use of the data by other researchers or made available to the public in a de‐identified state. However, there may be questions of the study participants as to whether the data are truly de‐identified and untraceable back to the study participant since the participant would not be able to see the process actually being carried out in detail in all relevant circumstances. Using Raw Data in Context Simply being in possession of raw data is never enough to build on that data from a research perspective. Researchers need to understand the context of the scientific research study in which the data were collected. This research context includes (but is not limited to): (i) the scientific hypothesis of the research study, (ii) the inclusion and exclusion criteria selected by the research designers of the study to determine which study participants were included and which were excluded from the study, (iii) the methods used to collect the data and possibilities of errors in the data collection processes, and (iv) answers to questions regarding whether any data were excluded from the study data and why these data were excluded. Ownership Rights Over Data There are ownership rights over data. Ownership rights include court decision making related to the question which parties are to be considered as the owners of the data. Possible owners here include (but are not limited to) (i) the study sponsor whose only role was to fund that study, (ii) the principal investigator(s) who developed the scientific hypothesis, and (iii) individual study participants through special contracts made by individual participants who negotiate such contracts with study sponsors and principal investigators to share in the ownership of data. Examples of such data are participant sources of unique gene lines or unique cell lines to be used in future research. Objections to Data Sharing Finally, individual researchers may simply object to sharing data. A researcher’s arguments here may be simply stating that he or she has a right to fully analyze the data since the collection of the data was the researchers’ idea in the first place. These researchers may argue that they have the right to mine the data as fully as they can before deciding to make the data open to the public for others to analyze. Works Cited 1 Kaiser, J. ‘Making Clinical Data Widely Available.’Science 322.5899 (10 Oct. 2008): 217–8. 2 de Lusignan, S., J.F. Metsemakers, P. Houwink, V. Gunnarsdottir, and J. van der Lei. ‘Routinely Collected General Practice Data: Goldmines for Research? A Report of the European Federation for Medical Informatics Primary Care Informatics Working Group (EFMI PCIWG) from MIE2006, Maastricht, The Netherlands.’Informatics in Primary Care 14.3 (2006): 203–9. 3 Sim, I. ‘Human Studies Database Project’, CTSA Data Repositories Symposium (17 Oct. 2008). 23 Nov. 2010. 4 Sim, I., C.G. Chute, H. Lehmann, R. Nagarajan, M. Nahm, and R.H. Scheuermann. ‘Keeping Raw Data in Context.’Science 323.5915 (6 Feb. 2009): 713. 5 U.S. Department of Health and Human Services, National Institutes of Health, HIPAA Privacy Rule, Information for Researchers. 13 Nov. 2010. 6 Wilczynski, N.L., R.B. Haynes, and Hedges Team. ‘EMBASE Search Strategies for Identifying Methodologically Sound Diagnostic Studies for Use by Clinicians and Researchers.’BMC Medicine 29.3 (March 2005): 7. (shrink)

In recent years, the amount of data in the world is growing rapidly. Data growth also occurs in the government sector. All ministries and institutions at every level are data producers. These government-owned data have a high potential if they can be used properly. Open government data can stimulate innovation and economic growth and enhance business models. In order to increase the willingness of citizens to use open government data and enjoy the benefits mentioned, (...) the quality of open government data needs to be improved. The quality of open government data encompasses a variety of dimensions and criteria. Also, the importance of each dimension and criterion in increasing the quality of open government data is different. Therefore, we are faced with a complex system that requires proper decision-making and management. In fact, we are dealing with decision-making in the complex management system. Given the importance of this issue, the purpose of this study is to provide a new and comprehensive method to improve the quality of open government data and increase the willingness of citizens to use the data by considering the complex network of citizens and organizations. For this purpose, library studies have been used to extract comprehensive and effective dimensions and criteria. The statistical population includes all articles related to the criteria of improving the quality of open government data and increasing the willingness of citizens to use the data. The probabilistic sampling method of simple random samples has been used, and 10 articles in this field have been reviewed. After extracting the criteria as well as the data of 112 governmental organizations and institutions related to each criterion from the open data portal, the complex network of citizens and governmental organizations and institutions has been analyzed in order to identify high-degree centrality organizations. Then, the data characteristics of the organizations that were most desired by the citizens were extracted using data mining techniques including the regression model. Also, field method and multicriteria decision-making technique including the DEMATEL technique have been used to express the solutions and identify the cause-and-effect relationships between the solutions. The criteria extracted in improving the quality of open government data and increasing the willingness of citizens to use the data are included: “data originality,” “license openness,” “up-to-datedness,” “data access,” “metadata completeness,” “number of data sets,” “format openness,” “nondiscrimination,” “understandable,” “number of categories of data sets,” “free,” “lack of missing data,” “data request ability,” “visualization,” “feedback,” and “data subject matter.” Based on the results obtained from the analysis of the complex network and the regression model, the criterion of “society subject” with a coefficient of 72.564 and a positive sign has the greatest impact on increasing the number of citizens' visits to open government data. After that, the criterion of “format openness” with a coefficient of 52.682 and a positive sign has the second rank in increasing the number of visits. Extracting comprehensive and effective criteria in improving the quality of open government data and increasing citizens' willingness to use data, calculating the weight and importance of each criterion by analyzing the complex network of citizens and organizations, as well as providing solutions, can help managers in decision-making and proper management in the complex system of citizens and government organizations. (shrink)

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation (...) system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered. (shrink)

Ontological realism aims at the development of high quality ontologies that faithfully represent what is general in reality and to use these ontologies to render heterogeneous data collections comparable. To achieve this second goal for clinical research datasets presupposes not merely (1) that the requisite ontologies already exist, but also (2) that the datasets in question are faithful to reality in the dual sense that (a) they denote only particulars and relationships between particulars that do in fact exist (...) and (b) they do this in terms of the types and type-level relationships described in these ontologies. While much attention has been devoted to (1), work on (2), which is the topic of this paper, is comparatively rare. Using Referent Tracking as basis, we describe a technical data wrangling strategy which consists in creating for each dataset a template that, when applied to each particular record in the dataset, leads to the generation of a collection of Referent Tracking Tuples (RTT) built out of unique identifiers for the entities described by means of the data items in the record. The proposed strategy is based on (i) the distinction between data and what data are about, and (ii) the explicit descriptions of portions of reality which RTTs provide and which range not only over the particulars described by data items in a dataset, but also over these data items themselves. This last feature allows us to describe particulars that are only implicitly referred to by the dataset; to provide information about correspondences between data items in a dataset; and to assert which data items are unjustifiably or redundantly present in or absent from the dataset. The approach has been tested on a dataset collected from patients seeking treatment for orofacial pain at two German universities and made available for the NIDCR-funded OPMQoL project. (shrink)

BackgroundPreprint usage is growing rapidly in the life sciences; however, questions remain on the relative quality of preprints when compared to published articles. An objective dimension of quality that is readily measurable is completeness of reporting, as transparency can improve the reader’s ability to independently interpret data and reproduce findings.MethodsIn this observational study, we initially compared independent samples of articles published in bioRxiv and in PubMed-indexed journals in 2016 using a quality of reporting questionnaire. After that, (...) we performed paired comparisons between preprints from bioRxiv to their own peer-reviewed versions in journals.ResultsPeer-reviewed articles had, on average, higher quality of reporting than preprints, although the difference was small, with absolute differences of 5.0% [95% CI 1.4, 8.6] and 4.7% [95% CI 2.4, 7.0] of reported items in the independent samples and paired sample comparison, respectively. There were larger differences favoring peer-reviewed articles in subjective ratings of how clearly titles and abstracts presented the main findings and how easy it was to locate relevant reporting information. Changes in reporting from preprints to peer-reviewed versions did not correlate with the impact factor of the publication venue or with the time lag from bioRxiv to journal publication.ConclusionsOur results suggest that, on average, publication in a peer-reviewed journal is associated with improvement in quality of reporting. They also show that quality of reporting in preprints in the life sciences is within a similar range as that of peer-reviewed articles, albeit slightly lower on average, supporting the idea that preprints should be considered valid scientific contributions. (shrink)

Medical-legal partnerships connect legal advocates to healthcare providers and settings. Maintaining effectiveness of medical-legal partnerships and consistently identifying opportunities for innovation and adaptation takes intentionality and effort. In this paper, we discuss ways in which our use of data and quality improvement methods have facilitated advocacy at both patient (client) and population levels as we collectively pursue better, more equitable outcomes.

This paper reflects on quality assessment and performance evaluation in higher education, namely by analysing the insufficient link between those two aspects. We start by reviewing the current state of the art regarding different processes and mechanisms of quality assessment and performance evaluation and discuss some of the major issues regarding the implementation of some of them. In particular, we analyse the current limitations regarding data collected, available and publicised on the performance of HEIs and the problems (...) those limitations bring to a fair evaluation of higher education. Through this analysis we intend to contribute to a better understanding of the mechanisms of evaluation in higher education and the way these may lead to the promotion of better quality assessment practices and institutional management. (shrink)

The integrity of biomedical research depends heavily on the quality of research data collected. In turn, data quality depends on processes of data collection, a task undertaken by frontline research staff in many research programmes in Africa and elsewhere. These frontline research staff often have additional responsibilities including translating and communicating research in local languages, seeking informed consent for study participation and maintaining supportive relationships between research institutions and study participants and wider communities. The level (...) of skills that fieldworkers need to undertake these responsibilities clearly affects the quality of data collected, the ethics of research ‘on the ground’ and the short and long term acceptability of research.We organised an international workshop in Kenya in July 2014 to discuss the role of frontline staff in scientific research. A total of 25 field managers from 9 African countries and the UK met for 2.5 days to discuss the relationship between data quality and institutional performance management systems and how they affect career progression and supportive supervision policies of research frontline staff.From this workshop, and supporting an expanding literature on the role of fieldworkers in international health research, participants agreed that fieldworkers’ roles present them with practical and ethical challenges that their routine training does not adequately prepare them for. We argue that the common and complex challenges facing fieldworkers should in part be addressed through increased investment and collaborative agreements across types of research institutions in Africa. We call for standardization of core elements of training for this critically important cadre of research staff who perform similar roles and encounter similar challenges in many African settings. Although many valuable training elements are offered in institutions, there is a need to develop broader, more grounded and innovative strategies to address complex realities for fieldworkers, and support the integrity and ethics of health research in these settings. (shrink)

Medical decision making relies heavily on the notion of the quality of a person's life in providing a reason to utilize one therapy over another or to withhold certain therapies altogether. The concept of quality of life, however, lacks clear definition as well as consensus on its constitutive elements. ;Chapter one engages a basic controversy in the literature: whether 'quality of life' should be understood as a subjective concept, or whether objective "lists" can help us assess a (...) person's quality of life. Chapter two examines whether anthropological data might provide guidance. While it is often argued that anthropological data supports relativism, John Finnis argues that anthropological data supports universalism which, in turn, bolsters objectivist claims. I argue that anthropological data may plausibly be interpreted to support either subjective or objective accounts, and thus, does not settle our approach to quality of life. ;Chapter Three examines objective list theories, in particular those of Bernard Gert and John Finnis. I find Gert's account to be problematic because it seems incomplete. Finnis's criteria, too, seems ultimately indefensible. Thus, I conclude that while objectivist claims may offer insight into human values and motivations, existing accounts are flawed. Chapter Four examines subjective accounts of quality of life. I focus on "preference satisfaction" accounts. While these accounts provide intuitively plausible descriptions, they fall victim to a host of difficulties. Perhaps the best example of this is the "fleeting preferences" problem, which undermines our confidence in making life-and-death decisions on the basis of quality of life considerations. ;The concluding chapter offers that much progress can be made by understanding quality of life in terms of finding meaning in life. Thus, the quality of one's life is determined by how much purpose and meaning is found in that life. While this notion is not without its problems, it does capture the complex elements of the kinds of cases in which we are particularly interested, and combines some of the best features of both the objective and subjective accounts. (shrink)

Background:The ability to save the life of an extremely premature baby has increased substantially over the last decade. This survival, however, can be associated with unfavourable outcomes for both baby and family. Questions are now being asked about quality of life for survivors of extreme prematurity. Quality of life is rightly deemed to be an important consideration in high technology neonatal care; yet, it is notoriously difficult to determine or predict. How does one define and operationalise what is (...) considered to be in the best interest of a surviving extremely premature baby, especially when the full extent of the outcomes might not be known for several years?Research question:The research investigates the caregiving dilemmas often faced by neonatal nurses when caring for extremely premature babies. This article explores the issues arising for neonatal nurses when they considered the philosophical and ethical questions about quality of life in babies ≤24 weeks gestation.Participants:Data were collected via a questionnaire to Australian neonatal nurses and semi-structured interviews with 24 neonatal nurses in New South Wales, Australia.Ethical considerations:Ethical processes and procedures have been adhered to by the researchers.Findings:A qualitative approach was used to analyse the data. The theme ‘difficult choices’ was generated which comprised three sub-themes: ‘damaged through survival’, ‘the importance of the brain’ and ‘families are important’. The results show that neonatal nurses believed that quality of life was an important consideration; yet they experienced significant inner conflict and uncertainty when asked to define or suggest specific elements of quality of life, or to suggest how it might be determined. It was even more difficult for the nurses to say when an extremely premature baby’s life possessed quality. Their previous clinical and personal experiences led the nurses to believe that the quality of the family’s life was important, and possibly more so than the quality of life of the surviving baby. This finding contrasts markedly with much of the existing literature in this field.Conclusion:Quality of life for extremely premature babies was an important consideration for neonatal nurses; however, they experienced difficulty deciding how to operationalise such considerations in their everyday clinical practice. (shrink)

Experiences of all kinds have a distinctive character, which marks them out as intrinsically different from states of consciousness such as thinking. A plausible view is that the difference should be accounted for by the fact that, in having an experience, the subject is somehow immediately aware of a range of phenomenal qualities. For example, in seeing, grasping and tasting an apple, the subject may be aware of a red and green spherical shape, a certain feeling of smoothness to touch, (...) and a sweet sensation. Such phenomenal qualities are also immediately present in hallucinations. According to the sense-data theory, phenomenal qualities belong to items called “sense-data.” In having a perceptual experience the subject is directly aware of, or acquainted with, a sense-datum, even if the experience is illusory or hallucinatory. The sense-datum is an object immediately present in experience. It has the qualities it appears to have. (shrink)

_The assessment of institutional quality and its influence on economic performance is highly relevant in Nigeria due to the country's constantly changing governmental institutions, dynamic market circumstances, and diversified socioeconomic atmosphere. Thus, the study aims to investigate the impact of institutional quality on the economic performance of Nigeria. This study employed ex post facto research, while time series data was used, which spans from 1996 to 2021, sourced from the Central Bank of Nigeria (CBN) and the Worldwide (...) Governance Indicators (WGI). The study employed unit root tests, Johansen cointegration, and the Error Correction Model as estimation techniques. However, post estimation tests such as normality, serial correlation, heteroskedasticity and stability tests were carried to ascertain the level of statistical accuracy outcome of the variables used. The findings suggest that there is a sustained nexus between the quality of institutions and characteristics related to economic growth. Also, it was equally found out that political stability has a significant impact in the short run. This suggests that a unit increase in political instability would definitely amount to a decrease in economic performance in Nigeria during the period under investigation. The study suggests enhancing accountability, transparency, administrative efficiency, and law adherence to improve Nigerian institutions. This can be achieved through reform implementation, capacity building, and good governance mechanisms, while reducing political unrest and fostering stability._. (shrink)

The notion of quality is analysed for its functional roots as a social heuristic for reusing others’ quality judgements and hence aiding choice. This is applied to the context of academic publishing, where the costs of publishing have greatly decreased, but the problem of finding the papers one wants has become harder. This paper suggests that instead of relying on generic quality judgements, such as those delivered by journal reviewers, that the maximum amount of judgemental information be (...) preserved and then made available to potential readers to help them find papers that meet their particular needs. The suggestion is that: multidimensional quality data be captured on review of papers, this information is stored on a database, and then used to filter papers according to the criteria set by the searcher—personalising the quality filter. In other words the quality judgements and subsequent use are maintained in a disaggregated form, maintaining the maximum informational context of the judgements for future use. The advantages, disadvantages, challenges and possible variations of this proposal are discussed. (shrink)

The growth of individuals and society heavily relies on education. Certain hindrances may prompt some students to halt their academic pursuits temporarily. This is known as "Balik-aral." The exploration of the quality of life, lived experiences, and challenges of grade 12 Balik-aral students was undertaken by the authors to break their silence and help them by developing recommendations that could be presented to the school's key stakeholders. A phenomenological approach was used to understand the phenomenon in a study involving (...) five grade 12 balik-aral students from different strands currently enrolled in a public school in Cebu City, Philippines. A structured interview was used to collect data, and significant themes emerged during data analysis using Colaizzi's method. The study revealed several sub-themes, including age disparities and pressure to obtain the diploma while maintaining quality of life. Additionally, key informants emphasized the sense of community and an immersive learning environment in their lived experiences as balik-aral students. Finally, competing priorities and scarcity of resources emerged as significant challenges for those involved in the study. Consequently, the investigation has determined that balik-aral students must be treated equitably to foster their engagement with their peers and enhance their self-assurance, ultimately leading to academic success. (shrink)