Managing the Socially Marginalized: Attitudes Towards Welfare, Punishment and Race

Welfare and incarceration policies have converged to form a system of governance over socially marginalized groups, particularly racial minorities. In both of these policy areas, rehabilitative and social support objectives have been replaced with a more punitive and restrictive system. The authors examine the convergence in individual-level attitudes concerning welfare and criminal punishment, using national survey data. The authors\u27 analysis indicates a statistically significant relationship between punitive attitudes toward welfare and punishment. Furthermore, accounting for the respondents\u27 racial attitudes explains the bivariate relationship between welfare and punishment. Thus, racial attitudes seemingly link support for punitive approaches to opposition to welfare expenditures. The authors discuss the implications of this study for welfare and crime control policies by way of the conclusion

Observation of a Coherence Length Effect in Exclusive Rho^0 Electroproduction

Exclusive incoherent electroproduction of the rho^0(770) meson from 1H, 2H, 3He, and 14N targets has been studied by the HERMES experiment at squared four-momentum transfer Q**2>0.4 GeV**2 and positron energy loss nu from 9 to 20 GeV. The ratio of the 14N to 1H cross sections per nucleon, known as the nuclear transparency, was found to decrease with increasing coherence length of quark-antiquark fluctuations of the virtual photon. The data provide clear evidence of the interaction of the quark- antiquark fluctuations with the nuclear medium.Comment: RevTeX, 5 pages, 3 figure

Determination of the Deep Inelastic Contribution to the Generalised Gerasimov-Drell-Hearn Integral for the Proton and Neutron

The virtual photon absorption cross section differences [sigma_1/2-sigma_3/2] for the proton and neutron have been determined from measurements of polarised cross section asymmetries in deep inelastic scattering of 27.5 GeV longitudinally polarised positrons from polarised 1H and 3He internal gas targets. The data were collected in the region above the nucleon resonances in the kinematic range nu < 23.5 GeV and 0.8 GeV**2 < Q**2 < 12 GeV**2. For the proton the contribution to the generalised Gerasimov-Drell-Hearn integral was found to be substantial and must be included for an accurate determination of the full integral. Furthermore the data are consistent with a QCD next-to-leading order fit based on previous deep inelastic scattering data. Therefore higher twist effects do not appear significant.Comment: 6 pages, 3 figures, 1 table, revte

MagAO Imaging of Long-period Objects (MILO). II. A Puzzling White Dwarf around the Sun-like Star HD 11112

The version of record, Rodigas, T. J. et al, 'MagAO Imaging of long-period objects (MILO). II. A puzzling white dwarf around the sun-like star HD 11112', The Astrophysical Journal, 831:177, November 2016, is available online via doi: 10.3847/0004-637X/831/2/177 © 2016. The American Astronomical Society. All rights reserved.HD 11112 is an old, Sun-like star that has a long-term radial velocity (RV) trend indicative of a massive companion on a wide orbit. Here we present direct images of the source responsible for the trend using the Magellan Adaptive Optics system. We detect the object (HD 11112B) at a separation of 2\fasec 2 (100 AU) at multiple wavelengths spanning 0.6-4 \microns ~and show that it is most likely a gravitationally-bound cool white dwarf. Modeling its spectral energy distribution (SED) suggests that its mass is 0.9-1.1 \msun, which corresponds to very high-eccentricity, near edge-on orbits from Markov chain Monte Carlo analysis of the RV and imaging data together. The total age of the white dwarf is $>2\sigma$ discrepant with that of the primary star under most assumptions. The problem can be resolved if the white dwarf progenitor was initially a double white dwarf binary that then merged into the observed high-mass white dwarf. HD 11112B is a unique and intriguing benchmark object that can be used to calibrate atmospheric and evolutionary models of cool white dwarfs and should thus continue to be monitored by RV and direct imaging over the coming years.Peer reviewedFinal Published versio

Depression prevalence using the HADS-D compared to SCID major depression classification:An individual participant data meta-analysis

Objectives: Validated diagnostic interviews are required to classify depression status and estimate prevalence of disorder, but screening tools are often used instead. We used individual participant data meta-analysis to compare prevalence based on standard Hospital Anxiety and Depression Scale – depression subscale (HADS-D) cutoffs of ≥8 and ≥11 versus Structured Clinical Interview for DSM (SCID) major depression and determined if an alternative HADS-D cutoff could more accurately estimate prevalence. Methods: We searched Medline, Medline In-Process & Other Non-Indexed Citations via Ovid, PsycINFO, and Web of Science (inception-July 11, 2016) for studies comparing HADS-D scores to SCID major depression status. Pooled prevalence and pooled differences in prevalence for HADS-D cutoffs versus SCID major depression were estimated. Results: 6005 participants (689 SCID major depression cases) from 41 primary studies were included. Pooled prevalence was 24.5% (95% Confidence Interval (CI): 20.5%, 29.0%) for HADS-D ≥8, 10.7% (95% CI: 8.3%, 13.8%) for HADS-D ≥11, and 11.6% (95% CI: 9.2%, 14.6%) for SCID major depression. HADS-D ≥11 was closest to SCID major depression prevalence, but the 95% prediction interval for the difference that could be expected for HADS-D ≥11 versus SCID in a new study was −21.1% to 19.5%. Conclusions: HADS-D ≥8 substantially overestimates depression prevalence. Of all possible cutoff thresholds, HADS-D ≥11 was closest to the SCID, but there was substantial heterogeneity in the difference between HADS-D ≥11 and SCID-based estimates. HADS-D should not be used as a substitute for a validated diagnostic interview.This study was funded by the Canadian Institutes of Health Research (CIHR, KRS-144045 & PCG 155468). Ms. Neupane was supported by a G.R. Caverhill Fellowship from the Faculty of Medicine, McGill University. Drs. Levis and Wu were supported by Fonds de recherche du Québec - Santé (FRQS) Postdoctoral Training Fellowships. Mr. Bhandari was supported by a studentship from the Research Institute of the McGill University Health Centre. Ms. Rice was supported by a Vanier Canada Graduate Scholarship. Dr. Patten was supported by a Senior Health Scholar award from Alberta Innovates, Health Solutions. The primary study by Scott et al. was supported by the Cumming School of Medicine and Alberta Health Services through the Calgary Health Trust, and funding from the Hotchkiss Brain Institute. The primary study by Amoozegar et al. was supported by the Alberta Health Services, the University of Calgary Faculty of Medicine, and the Hotchkiss Brain Institute. The primary study by Cheung et al. was supported by the Waikato Clinical School, University of Auckland, the Waikato Medical Research Foundation and the Waikato Respiratory Research Fund. The primary study by Cukor et al. was supported in part by a Promoting Psychological Research and Training on Health-Disparities Issues at Ethnic Minority Serving Institutions Grants (ProDIGs) awarded to Dr. Cukor from the American Psychological Association. The primary study by De Souza et al. was supported by Birmingham and Solihull Mental Health Foundation Trust. The primary study by Honarmand et al. was supported by a grant from the Multiple Sclerosis Society of Canada. The primary study by Fischer et al. was supported as part of the RECODEHF study by the German Federal Ministry of Education and Research (01GY1150). The primary study by Gagnon et al. was supported by the Drummond Foundation and the Department of Psychiatry, University Health Network. The primary study by Akechi et al. was supported in part by a Grant-in-Aid for Cancer Research (11−2) from the Japanese Ministry of Health, Labour and Welfare and a Grant-in-Aid for Young Scientists (B) from the Japanese Ministry of Education, Culture, Sports, Science and Technology. The primary study by Kugaya et al. was supported in part by a Grant-in-Aid for Cancer Research (9–31) and the Second-Term Comprehensive 10-year Strategy for Cancer Control from the Japanese Ministry of Health, Labour and Welfare. The primary study Ryan et al. was supported by the Irish Cancer Society (Grant CRP08GAL). The primary study by Keller et al. was supported by the Medical Faculty of the University of Heidelberg (grant no. 175/2000). The primary study by Love et al. (2004) was supported by the Kathleen Cuningham Foundation (National Breast Cancer Foundation), the Cancer Council of Victoria and the National Health and Medical Research Council. The primary study by Love et al. (2002) was supported by a grant from the Bethlehem Griffiths Research Foundation. The primary study by Löwe et al. was supported by the medical faculty of the University of Heidelberg, Germany (Project 121/2000). The primary study by Navines et al. was supported in part by the Spanish grants from the Fondo de Investigación en Salud, Instituto de Salud Carlos III (EO PI08/90869 and PSIGEN-VHC Study: FIS-E08/00268) and the support of FEDER (one way to make Europe). The primary study by O'Rourke et al. was supported by the Scottish Home and Health Department, Stroke Association, and Medical Research Council. The primary study by Sanchez-Gistau et al. was supported by a grant from the Ministry of Health of Spain (PI040418) and in part by Catalonia Government, DURSI 2009SGR1119. The primary study by Gould et al. was supported by the Transport Accident Commission Grant. The primary study by Rooney et al. was supported by the NHS Lothian Neuro-Oncology Endowment Fund. The primary study by Schwarzbold et al. was supported by PRONEX Program (NENASC Project) and PPSUS Program of Fundaçao de Amparo a esquisa e Inovacao do Estado de Santa Catarina (FAPESC) and the National Science and Technology Institute for Translational Medicine (INCT-TM). The primary study by Simard et al. was supported by IDEA grants from the Canadian Prostate Cancer Research Initiative and the Canadian Breast Cancer Research Alliance, as well as a studentship from the Canadian Institutes of Health Research. The primary study by Singer et al. (2009) was supported by a grant from the German Federal Ministry for Education and Research (no. 01ZZ0106). The primary study by Singer et al. (2008) was supported by grants from the German Federal Ministry for Education and Research (# 7DZAIQTX) and of the University of Leipzig (# formel. 1–57). The primary study by Meyer et al. was supported by the Federal Ministry of Education and Research (BMBF). The primary study by Stone et al. was supported by the Medical Research Council, UK and Chest Heart and Stroke, Scotland. The primary study by Turner et al. was supported by a bequest from Jennie Thomas through Hunter Medical Research Institute. The primary study by Walterfang et al. was supported by Melbourne Health. Drs. Benedetti and Thombs were supported by FRQS researcher salary awards. No other authors reported funding for primary studies or for their work on this study. No funder had any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication

Defining Quality Indicators for Breast Device Surgery: Using Registries for Global Benchmarking

Background: Breast device registries monitor devices encompassing breast implants, tissue expanders and dermal matrices, and the quality of care and patient outcomes for breast device surgery. Defining a standard set of quality indicators and risk adjustment factors will enable consistency and adjustment for case-mix in benchmarking quality of care across breast implant registries. This study aimed to develop a set of quality indicators to enable assessment and reporting of quality of care for breast device surgery which can be applied globally. Methods: A scoping literature review was undertaken, and potential quality indicators were identified. Consensus on the final list of quality indicators was obtained using a modified Delphi approach. This process involved a series of online surveys, and teleconferences over 6 months. The Delphi panel included participants from various countries and representation from surgical specialty groups including breast and general surgeons, plastic and reconstructive surgeons, cosmetic surgeons, a breast-care nurse, a consumer, a devices regulator (Therapeutic Goods Administration), and a biostatistician. A total of 12 candidate indicators were proposed: Intraoperative antibiotic wash, intraoperative antiseptic wash, preoperative antibiotics, nipple shields, surgical plane, volume of implant, funnels, immediate versus delayed reconstruction, time to revision, reoperation due to complications, patient satisfaction, and volume of activity. Results: Three of the 12 proposed indicators were endorsed by the panel: preoperative intravenous antibiotics, reoperation due to complication, and patient reported outcome measures. Conclusion: The 3 endorsed quality indicator measures will enable breast device registries to standardize benchmarking of care internationally for patients undergoing breast device surgery

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI