The outlined empirical study on the decision-making process concerning the limitation of life-prolonging treatment (DLT) in patients with advanced cancer at a University hospital setting triggered some new questions for research ethics with respect to studies using empirical methods in medical ethics. The analyzed data of the study showed that less than half of the patients were involved in DLT. Deciding against CPR (cardiopulmonary resuscitation) and transferal to the ICU (intensive care unit) without informing and explaining it to the perfectly competent patient can be regarded as a violation of the ethical principle of respect for autonomy. This is what the embedded researcher witnessed throughout the study recruitment and data acquisition, as the noninvolvement of patients was not just a result of the final data analysis. The ethical question raised in this situation was as follows: Does the embedded researcher has a moral duty to intervene when she witnesses that ethical standards are not upheld?
Pluralism has become the defining characteristic of many modern societies. Not only a plurality of individual and social claims and activities gain impacts on societal life. A creative pluralism of institutions and their norms profoundly shape our moral commitments and character – notably the family, the market, the media, and systems of law, religion, politics, research, education, health care, and defense. In the theoretical, empirical, and historical contributions to this volume, specialists on medicine, medical ethics, psychology, theology and health care discuss the many challenges that major transformations in their areas of expertise pose to the communication and orientation in late modern pluralistic societies. Contributors come from Germany, the USA and Australia.
Verfügbarkeit an Ihrem Standort wird überprüft
Dieses Buch ist auch in Ihrer Bibliothek verfügbar:
We conducted this realist evaluation study of an organizational health intervention involving 421 low-skilled workers (50% female), half of whom were immigrants, in three companies over six months. Non-profit agencies implemented peer-mentoring and taught peer-mentors and line managers how to enhance social support in order to improve workers' work situation in a participative way. We formulated five mechanisms of change: the company management encouragement mechanism, the role model mechanism, the peer-mentor support mechanism, the line manager support mechanism, and the participative work improvement mechanism. We combined realist evaluation with a quasi-experimental design and process evaluation in a multi-methods approach. Results of multiple group latent change models and qualitative research showed that intervention-group workers perceived increases in peer-mentor support but not in line manager support. Peer-mentors managed to initiate high-quality improvements at work. Intervention-group workers showed significant reductions in blood pressure. Control-group workers experienced more psychosomatic complaints over time in significant contrast to intervention-group workers. Our results suggest that peer-mentoring offers an effective way for low-skilled workers and immigrants to achieve better health. To improve such health effects, a greater focus on line managers' work situations is needed to help them provide support.
In response to the ongoing coronavirus disease 2019 (COVID-19) pandemic, governments imposed various measures to decrease the rate of disease spread, and health care policy makers prioritized resource allocation to accommodate COVID-19 patients. We conducted a cross-sectional online survey in Germany (July 2020–June 2021) to assess the frequency of changes to cancer care among cancer patients and to explore the psychological impact of the pandemic writ large. Cancer patients who contacted the Cancer Information Service (Krebsinformationsdienst, KID) of the German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ) via email were invited to complete an online questionnaire, capturing demographics, cancer specifics (e.g., type, disease phase, primary place of treatment, etc.), and any changes to their medical, follow-up, psycho-oncological or nursing care. General level of psychological distress was measured using the Hospital Anxiety and Depression Scale (HADS) along with face-validated items regarding worries and social isolation specific to the pandemic. In total, 13% of 621 patients reported a change to their treatment or care plan. Of those patients with changes, the majority of changes were made to follow-up care after treatment (56%), to monitoring during treatment (29%) and to psychological counseling (20%). Of the overall sample, more than half of patients (55%) reported symptoms of anxiety and 39% reported symptoms of depression. Patients with a change in cancer care were more likely to report symptoms of depression than those with no change (AOR: 2.18; 95% CI: 1.26–3.76). Concern about the pandemic affecting the quality of health care was a predictor of both anxiety (AOR: 2.76; 95% CI: 1.75–4.35) and depression (AOR: 2.15; 95% CI: 1.43–3.23). Results showed that the majority of cancer patients in our study did not experience a change in their cancer care. However, the level of anxiety and psycho-social burden of cancer patients during the pandemic was high throughout the study period. Our ...
Das Kapitel fasst zunächst den aktuellen Sachstand zusammen und bietet einen Ausblick auf die zukünftige Bedeutung von Organoiden für Forschung und Gesundheitsversorgung, wobei auch die Rechtslage und ethische Diskussionen berücksichtigt werden. Daran anschließend werden Handlungsempfehlungen für die Politik abgeleitet.
Telemedizin, datenbasierte Gesundheitsanalysen, Health-Apps und mobile Geräte zur individuellen Gesundheitskontrolle – immer mehr Gesundheitsdienstleistungen werden mit Hilfe digitaler Dienste und Strukturen angeboten. Vor diesem Hintergrund beleuchten Daten- und Verbraucherschützer, Mediziner und Gesundheitsapp-Entwickler, Rechts- und Gesellschaftswissenschaftler die fachliche Vielfalt der Thematik – und geben viele konkrete Impulse, wie E-Health-Innovationen gefördert und zugleich die Rechte und Interessen von Patienten gewahrt werden
Zugriffsoptionen:
Die folgenden Links führen aus den jeweiligen lokalen Bibliotheken zum Volltext:
Telemedizin, datenbasierte Gesundheitsanalysen, Health-Apps und mobile Geräte zur individuellen Gesundheitskontrolle - immer mehr Gesundheitsdienstleistungen werden mit Hilfe digitaler Dienste und Strukturen angeboten. Die rasante Entwicklung verspricht nicht nur enorme Qualitätssteigerungen in der Gesundheitsversorgung und neue Märkte im Gesundheitssektor. Sie wirft auch viele Fragen mit weitreichender Relevanz für den Datenschutz auf: - Wie sehen die wissenschaftlichen, aber auch die ökonomischen Perspektiven dieser Entwicklung aus? - Wie zuverlässig sind digitale Lösungsansätze im Gesundheitsbereich? - Wie entwickelt sich zukünftig das Arzt-Patienten-Verhältnis? - Welche gesellschaftlichen Folgen könnte eine "Kultur der Selbstvermessung" haben? - Wie kann das Vertrauen der Patienten, bzw. der Anwender in E-Health-Dienstleistungen nachhaltig gestärkt werden? Vor diesem Hintergrund beleuchten Daten- und Verbraucherschützer, Mediziner und Gesundheitsapp-Entwickler, Rechts- und Gesellschaftswissenschaftler die fachliche Vielfalt der Thematik - und geben viele konkrete Impulse, wie E-Health-Innovationen gefördert und zugleich die Rechte und Interessen von Patienten gewahrt werden. Das Buch erscheint als Band 2 der Reihe DatenDebatten der von der Bundesrepublik Deutschland gegründeten Stiftung Datenschutz
The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits. ; B.P.C. acknowledges funding from Abigail Wexner Research Institute at Nationwide Children's Hospital; T.H. Nyrönen acknowledges funding from Academy of Finland grant #31996; A.M.-J., K.N., T.F.B., O.M.H., and Z.S. acknowledge funding from Australian Medical Research Future Fund; M.S. acknowledges funding from Biobank Japan; D. Bujold and S.J.M.J. acknowledge funding from Canada Foundation for Innovation; L.J.D. acknowledges funding from Canada Foundation for Innovation Cyber Infrastructure grant #34860; D. Bujold and G.B. acknowledge funding from CANARIE; L.J.D. acknowledges funding from CANARIE Research Data Management contract #RDM-090 (CHORD) and #RDM2-053 (ClinDIG); K.K.-L. acknowledges funding from CanSHARE; T.L.T. acknowledges funding from Chan Zuckerberg Initiative; T. Burdett acknowledges funding from Chan Zuckerberg Initiative grant #2017-171671; D. Bujold, G.B., and L.D.S. acknowledge funding from CIHR; L.J.D. acknowledges funding from CIHR grant #404896; M.J.S.B. acknowledges funding from CIHR grant #SBD-163124; M. Courtot and M. Linden acknowledge funding from CINECA project EU Horizon 2020 grant #825775; D. Bujold and G.B. acknowledge funding from Compute Canada; F.M.-G. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – NFDI 1/1 "GHGA – German Human Genome-Phenome Archive; R.M.H.-S. acknowledges funding from Duke-Margolis Center for Health Policy; S.B. and A.J.B. acknowledge funding from EJP-RD EU Horizon 2020 grant #825575; A. Niewielska, A.K., D.S., G.I.S., J.A.T., J.R., M.A.K., M. Baudis, M. Linden, S.B., S.S., T.H. Nyrönen, and T.M.K. acknowledge funding from ELIXIR; A. Niewielska acknowledges funding from EOSC-Life EU Horizon 2020 grant #824087; J.-P.H. acknowledges funding from ETH Domain Strategic Focal Area "Personalized Health and Related Technologies (PHRT)" grant #2017-201; F.M.-G. acknowledges funding from EUCANCan EU Horizon 2020 grant #825835; B.M.K., D. Bujold, G.B., L.D.S., M.J.S.B., N.S., S.E.W., and Y.J. acknowledge funding from Genome Canada; B.M.K., M.J.S.B., S.E.W., and Y.J. acknowledge funding from Genome Quebec; F.M.-G. acknowledges funding from German Human Genome-Phenome Archive; C. Voisin acknowledges funding from Google; A.J.B. acknowledges funding from Health Data Research UK Substantive Site Award; D.H. acknowledges funding from Howard Hughes Medical Institute; S.B. acknowledges funding from Instituto de Salud Carlos III; S.-S.K. and K.T. acknowledge funding from Japan Agency for Medical Research and Development (AMED); S. Ogishima acknowledges funding from Japan Agency for Medical Research and Development (AMED) grant #20kk0205014h0005; C.Y. and K. Kosaki acknowledge funding from Japan Agency for Medical Research and Development (AMED) grant #JP18kk0205012; GEM Japan acknowledges funding from Japan Agency for Medical Research and Development (AMED) grants #19kk0205014h0004, #20kk0205014h0005, #20kk0205013h0005, #20kk0205012h0005, #20km0405401h0003, and #19km0405001h0104; J.R. acknowledges funding from La Caixa Foundation under project #LCF/PR/GN13/50260009; R.R.F. acknowledges funding from Mayo Clinic Center for Individualized Medicine; Y.J. and S.E.W. acknowledge funding from Ministère de l'Économie et de l'Innovation du Québec for the Can-SHARE Connect Project; S.E.W. and S.O.M.D. acknowledge funding from Ministère de l'Économie et de l'Innovation du Québec for the Can-SHARE grant #141210; M.A.H., M.C.M.-T., J.O.J., H.E.P., and P.N.R. acknowledge funding from Monarch Initiative grant #R24OD011883 and Phenomics First NHGRI grant #1RM1HG010860; A.L.M. and E.B. acknowledge funding from MRC grant #MC_PC_19024; P.T. acknowledges funding from National University of Singapore and Agency for Science, Technology and Research; J.M.C. acknowledges funding from NHGRI; A.H.W. acknowledges funding from NHGRI awards K99HG010157, R00HG010157, and R35HG011949; A.M.-J., K.N., D.P.H., O.M.H., T.F.B., and Z.S. acknowledge funding from NHMRC grants #GNT1113531 and #GNT2000001; D.L.C. acknowledges funding from NHMRC Ideas grant #1188098; A.B.S. acknowledges funding from NHMRC Investigator Fellowship grant #APP177524; J.M.C. and L.D.S. acknowledge funding from NIH; A.A.P. acknowledges funding from NIH Anvil; A.V.S. acknowledges funding from NIH contract #HHSN268201800002I (TOPMed Informatics Research Center); S.U. acknowledges funding from NIH ENCODE grant #UM1HG009443; M.C.M.-T. and M.A.H. acknowledge funding from NIH grant #1U13CA221044; R.J.C. acknowledges funding from NIH grants #1U24HG010262 and #1U2COD023196; M.G. acknowledges funding from NIH grant #R00HG007940; J.B.A., S.L., P.G., E.B., H.L.R., and L.S. acknowledge funding from NIH grant #U24HG011025; K.P.E. acknowledges funding from NIH grant #U2C-RM-160010; J.A.E. acknowledges funding from NIH NCATS grant #U24TR002306; M.M. acknowledges funding from NIH NCI contract #HHSN261201400008c and ID/IQ Agreement #17X146 under contract #HHSN2612015000031 and #75N91019D00024; R.M.C.-D. acknowledges funding from NIH NCI grant #R01CA237118; M. Cline acknowledges funding from NIH NCI grant #U01CA242954; K.P.E. acknowledges funding from NIH NCI ITCR grant #1U24CA231877-01; O.L.G. acknowledges funding from NIH NCI ITCR grant #U24CA237719; R.L.G. acknowledges funding from NIH NCI task order #17X147F10 under contract #HHSN261200800001E; A.F.R. acknowledges funding from NIH NHGRI grant #RM1HG010461; N.M. and L.J.Z. acknowledge funding from NIH NHGRI grant #U24HG006941; R.R.F., T.H. Nelson, L.J.B., and H.L.R. acknowledge funding from NIH NHGRI grant #U41HG006834; B.J.W. acknowledges funding from NIH NHGRI grant #UM1HG009443A; M. Cline acknowledges funding from NIH NHLBI BioData Catalyst Fellowship grant #5118777; M.M. acknowledges funding from NIH NHLBI BioData Catalyst Program grant #1OT3HL142478-01; N.C.S. acknowledges funding from NIH NIGMS grant #R35-GM128636; M.C.M.-T., M.A.H., P.N.R., and R.R.F. acknowledge funding from NIH NLM contract #75N97019P00280; E.B. and A.L.M. acknowledge funding from NIHR; R.G. acknowledges funding from Project Ris3CAT VEIS; S.B. acknowledges funding from RD-Connect, Seventh Framework Program grant #305444; J.K. acknowledges funding from Robertson Foundation; S.B. and A.J.B. acknowledge funding from Solve-RD, EU Horizon 2020 grant #779257; T.S. and S. Oesterle acknowledge funding from Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN), supported by the Swiss State Secretariat for Education, Research and Innovation SERI; S.J.M.J. acknowledges funding from Terry Fox Research Institute; A.E.H., M.P.B., M. Cupak, M.F., and J.F. acknowledge funding from the Digital Technology Supercluster; D.F.V. acknowledges funding from the Australian Medical Research Future Fund, as part of the Genomics Health Futures Mission grant #76749; M. Baudis acknowledges funding from the BioMedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN); B.M.K. acknowledges funding from the Canada Research Chair in Law and Medicine and CIHR grant #SBD-163124; D.S., G.I.S., M.A.K., S.B., S.S., and T.H. Nyrönen acknowledge funding from the EU Horizon 2020 Beyond 1 Million Genomes (B1MG) Project grant #951724; P.F., A.D.Y., F.C., H.S., I.U.L., D. Gupta, M. Courtot, S.E.H., T. Burdett, T.M.K., and S.F. acknowledge funding from the European Molecular Biology Laboratory; Y.J. and S.E.W. acknowledge funding from the Government of Canada; P.G. acknowledges funding from the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-206); J.Z. acknowledges funding from the Government of Ontario; C.K.Y. acknowledges funding from the Government of Ontario, Canada Foundation for Innovation; C. Viner and M.M.H. acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (grant #RGPIN-2015-03948 to M.M.H. and Alexander Graham Bell Canada Graduate Scholarship to C.V.); K.K.-L. acknowledges funding from the Program for Integrated Database of Clinical and Genomic Information; J.K. acknowledges funding from the Robertson Foundation; D.F.V. acknowledges funding from the Victorian State Government through the Operational Infrastructure Support (OIS) Program; A.M.L., R.N., and H.V.F. acknowledge funding from Wellcome (collaborative award); F.C., H.S., P.F., and S.E.H. acknowledge funding from Wellcome Trust grant #108749/Z/15/Z; A.D.Y., H.S., I.U.L., M. Courtot, H.E.P., P.F., and T.M.K. acknowledge funding from Wellcome Trust grant #201535/Z/16/Z; A.M., J.K.B., R.J.M., R.M.D., and T.M.K. acknowledge funding from Wellcome Trust grant #206194; E.B., P.F., P.G., and S.F. acknowledge funding from Wellcome Trust grant #220544/Z/20/Z; A. Hamosh acknowledges funding from NIH NHGRI grant U41HG006627 and U54HG006542; J.S.H. acknowledges funding from National Taiwan University #91F701-45C and #109T098-02; the work of K.W.R. was supported by the Intramural Research Program of the National Library of Medicine, NIH. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. H.V.F. acknowledges funding from Wellcome Grant 200990/A/16/Z 'Designing, developing and delivering integrated foundations for genomic medicine'. ; Peer reviewed