Over the course of the 20th century the sex differential in life expectancy at birth in the industrialized countries has widened considerably in favour of women. Starting in the early 1970s, the beginning of a reversal in the long-term pattern of this differential has been noted in some high-income countries. This study documents a sustained pattern of narrowing of this measure into the later part of the 1990s for six of the populations that comprise the G7 countries: Canada, France, Germany, Italy, England and Wales (as representative of the United Kingdom) and USA. For Japan, a persistence of widening sex differences in survival is noted. The sex differences in life expectancy are decomposed over roughly three decades (early 1970s to late 1990s) from the point of view of four major cause-of-death categories: circulatory diseases, cancers, accidents/violence/suicide, and 'other' (residual) causes. In the six countries where the sex gap has narrowed, this has resulted primarily from reduced sex differences in circulatory disease mortality, and secondarily from reduced differences in male and female death rates due to accidents, violence and suicide combined. In some of the countries sex differentials in cancer mortality have been converging lately, and this has also contributed to a narrowing of the difference in life expectancy. In Japan, males have been less successful in reducing their survival disadvantage in relation to Japanese women with regard to circulatory disease and cancer; and in the case of accidents/violence/suicide, male death rates increased during the 1990s. These trends explain the divergent pattern of the sex difference in life expectation in Japan as compared with the other G7 nations.
This article explores the production and type of knowledge acquired in the course of specific digital self-tracking activities that resemble research and are common among followers of the Quantified Self movement. On the basis of interviews with self-trackers, it is shown that this knowledge can be characterised as a verified and practical self-knowledge, and that science in the form of scientific sources, methods and quality criteria plays a key role in its production. It is argued that this self-related knowledge can be conceptualised as self-expertise, and its production as personal science. The article then discusses the implications for the science-society relationship. In contrast to self-tracking data, so far self-knowledge has hardly caused any resonance in science, although science currently appears open to the insights from single subject (N-of-1) research. As a new mode of public engagement with science, personal science instead mainly leads to an individual self-expertisation.
Citizen science (CS) can foster transformative impact for science, citizen empowerment and socio-political processes. To unleash this impact, a clearer understanding of its current status and challenges for its development is needed. Using quantitative indicators developed in a collaborative stakeholder process, our study provides a comprehensive overview of the current status of CS in Germany, Austria and Switzerland. Our online survey with 340 responses focused on CS impact through (1) scientific practices, (2) participant learning and empowerment, and (3) socio-political processes. With regard to scientific impact, we found that data quality control is an established component of CS practice, while publication of CS data and results has not yet been achieved by all project coordinators (55%). Key benefits for citizen scientists were the experience of collective impact (“making a difference together with others”) as well as gaining new knowledge. For the citizen scientists’ learning outcomes, different forms of social learning, such as systematic feedback or personal mentoring, were essential. While the majority of respondents attributed an important value to CS for decision-making, only few were confident that CS data were indeed utilized as evidence by decision-makers. Based on these results, we recommend (1) that project coordinators and researchers strengthen scientific impact by fostering data management and publications, (2) that project coordinators and citizen scientists enhance participant impact by promoting social learning opportunities and (3) that project initiators and CS networks foster socio-political impact through early engagement with decision-makers and alignment with ongoing policy processes. In this way, CS can evolve its transformative impact.
Background Machine learning-based clinical decision support systems (ML_CDSS) are increasingly employed in various sectors of health care aiming at supporting clinicians’ practice by matching the characteristics of individual patients with a computerised clinical knowledge base. Some studies even indicate that ML_CDSS may surpass physicians’ competencies regarding specific isolated tasks. From an ethical perspective, however, the usage of ML_CDSS in medical practice touches on a range of fundamental normative issues. This article aims to add to the ethical discussion by using professionalisation theory as an analytical lens for investigating how medical action at the micro level and the physician–patient relationship might be affected by the employment of ML_CDSS. Main text Professionalisation theory, as a distinct sociological framework, provides an elaborated account of what constitutes client-related professional action, such as medical action, at its core and why it is more than pure expertise-based action. Professionalisation theory is introduced by presenting five general structural features of professionalised medical practice: (i) the patient has a concern; (ii) the physician deals with the patient’s concern; (iii) s/he gives assistance without patronising; (iv) s/he regards the patient in a holistic manner without building up a private relationship; and (v) s/he applies her/his general expertise to the particularities of the individual case. Each of these five key aspects are then analysed regarding the usage of ML_CDSS, thereby integrating the perspectives of professionalisation theory and medical ethics. Conclusions Using ML_CDSS in medical practice requires the physician to pay special attention to those facts of the individual case that cannot be comprehensively considered by ML_CDSS, for example, the patient’s personality, life situation or cultural background. Moreover, the more routinized the use of ML_CDSS becomes in clinical practice, the more that physicians need to focus on the patient’s concern and strengthen patient autonomy, for instance, by adequately integrating digital decision support in shared decision-making.
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