AppID Allow List

Louise Folkes

Within qualitative research, much can be learned from the influence of researcher positionality on the research process. Reflecting upon ethnographic fieldwork undertaken for a doctoral study, this paper explores how researcher positionality not only shapes research motivations but also situates the researcher and the ‘researched’, impacting how data is created and interpreted. There is a long history of engaging with positionality in qualitative research, however, oftentimes this engagement is purely descriptive, providing a ‘shopping list’ of characteristics and stating if these are shared or not with participants. It is important for engagement with reflexivity to go beyond providing a ‘shopping list’ of positionality statements to develop deeper discussions about the fluidity of positionality across the research process. Using the previously established concept of ‘kitchen table reflexivity’, I reflect on how talk allows researchers to outline shifts and adaptability in positionality as research progresses. I expand this concept to argue that kitchen table reflexivity can occur in conversations during fieldwork with participants, utilising a range of in/visible tools at the researcher’s disposal. For example, the spaces between fieldwork encounters, the ‘waiting field’, is often where observations and informal discussions with participants take place. Using fieldnotes and interview data, this paper outlines how positionality fluctuates and interweaves with the theoretical, methodological, and analytical approach taken. The paper concludes by restating the importance of meaningful engagement with positionality throughout qualitative research, in order to avoid static and hollow positionality statements.

Hiba Mohammed Taha, R. Aalizadeh, Nikiforos A Alygizakis, J. Antignac, H. Arp, Richard Bade, Nancy C. Baker, L. Belova, L. Bijlsma, Evan E. Bolton, W. Brack, A. Celma, Wen-Ling Chen, Tiejun Cheng, Parviel Chirsir, Ľ. Čirka, Lisa A. D’Agostino, Yannick Djoumbou Feunang, V. Dulio, Stellan Fischer, P. Gago-Ferrero, Aikaterini Galani, B. Geueke, N. Głowacka, Juliane Glüge, Ksenia J. Groh, S. Grosse, P. Haglund, P. Hakkinen, Sarah E. Hale, Félix Hernández, E. Janssen, T. Jonkers, Karin Kiefer, M. Kirchner, J. Koschorreck, M. Krauss, Jessy Krier, M. Lamoree, Marion Letzel, T. Letzel, Qingliang Li, J. Little, Yanna Liu, David M. Lunderberg, Jon W. Martin, Andrew Mceachran, J. McLean, C. Meier, J. Meijer, Frank Menger, Carla Merino, J. Muncke, Matthias Muschket, Michael Neumann, V. Neveu, Kelsey Ng, H. Oberacher, J. O'Brien, P. Oswald, Martina Oswaldova, Jaqueline A. Picache, C. Postigo, N. Ramírez, T. Reemtsma, J. Renaud, P. Rostkowski, H. Rüdel, R. Salek, S. Samanipour, M. Scheringer, Ivo Schliebner, W. Schulz, Tobias Schulze, M. Sengl, Benjamin A. Shoemaker, Kerry Sims, H. Singer, Randolph R. Singh, M. Sumarah, P. Thiessen, K. Thomas, Sònia Torres, X. Trier, A. V. van Wezel, R. Vermeulen, J. Vlaanderen, P. C. von der Ohe, Zhanyun Wang, Antony J. Williams, E. Willighagen, D. Wishart, Jian Zhang, N. Thomaidis, J. Hollender, J. Slobodnik, Emma L. Schymanski

Background The NORMAN Association ( https://www.norman-network.com/ ) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/ ) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for “suspect screening” lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide. Results The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community ( https://zenodo.org/communities/norman-sle ), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ) and the US EPA’s CompTox Chemicals Dashboard ( https://comptox.epa.gov/dashboard/ ), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser ( https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101 ). Conclusions The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the “one substance, one assessment” approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website ( https://www.norman-network.com/nds/SLE/ ).

Max R. Greenky, William L. Wang, D. Ponzio, P. Courtney

BACKGROUND The Centers for Medicare and Medicaid Services has solicited public comments for the 2019 Proposed Rule to remove total hip arthroplasty (THA) from the inpatient-only list. Concerns exist regarding the safety of discharging higher risk Medicare patients as an outpatient and whether hospitals may still be reimbursed for an inpatient procedure. The purpose of this study is to determine whether Medicare-aged patients undergoing outpatient THA have higher complication rates than patients who underwent inpatient THA. We also sought to identify characteristics of Medicare-aged patients that are associated with increased risk of complications or longer stay following short-stay THA. METHODS We queried the American College of Surgeons National Surgical Quality Improvement Program database for patients over age 65 who underwent primary THA between 2015 and 2016. We compared demographics, comorbidities, and 30-day complication, reoperation, and readmission rates among outpatient, short-stay, and inpatient groups. A multivariate regression analysis identified patients who are at an increased risk for complications and a longer inpatient stay following short-stay THA. RESULTS Of the 34,416 Medicare-aged patients who underwent THA, 310 (1%) were discharged on postoperative day 0, 5698 (16.5%) on postoperative day 1, and 28,408 (82.5%) were inpatients. The outpatient and short-stay patients had lower 30-day complication and readmission rates than the inpatient group. Independent risk factors for developing a complication or requiring an inpatient stay included general anesthesia, body mass index >35 kg/m2, diabetes, chronic obstructive pulmonary disease, congestive heart failure, hypertension, malnutrition, female gender, age >75 years, minority ethnicity, and an American Society of Anesthesiologists score of 4 (all P < .05). CONCLUSION Outpatient and short-stay THA appears to be safe in a small subset of Medicare-aged patients. Centers for Medicare and Medicaid Services should allow surgeons flexibility in determining admission status based on each patient's risk profile.

Aslı Kılıç, A. Criss, Kenneth J. Malmberg, R. Shiffrin

Differentiation is a theory that originally emerged from the perception literature and proposes that with experience, the representation of stimuli becomes more distinct from or less similar to the representation of other stimuli. In recent years, the role of differentiation has played a critical role in models of memory. Differentiation mechanisms have been implemented in episodic memory models by assuming that information about new experiences with a stimulus in a particular context accumulates in a single memory trace and these updated memory traces become more distinct from the representations of other stimuli. A key implication of such models is that well encoded events are less confusable with other events. This prediction is particularly relevant for two important phenomena. One is the role of encoding strength on memory. The strength based mirror effect is the finding of higher hit rates and lower false alarm rates for a list composed of all strongly encoded items compared to a list composed of all weakly encoded items. The other is output interference, the finding that accuracy decreases across a series of test trials. Results from four experiments show a tight coupling between these two empirical phenomena such that strongly encoded target items are less prone to interference. By proposing a process model and evaluating the predictions of the model, we show how a single theoretical principle, differentiation, provides a unified explanation for these effects.

B. Stahl, Tonii Leach

Ethical and social concerns are a key obstacle to the adoption of artificial intelligence (AI) in the life sciences and beyond. The discussion of these issues has intensified in recent years and led to a number of approaches, tools and initiatives. Key amongst them is the idea of ex-ante impact assessments that aim to identify issues at the early stages of development. One prominent example of such ex-ante impact assessment is the European Union's (EU) Assessment list for Trustworthy AI (ALTAI). This article uses the findings of a large-scale application of the ALTAI to a large neuro-informatics project as an exemplar to demonstrate the effectiveness and limitations of the ALTAI in practice. The article shows that ex-ante impact assessment has the potential to help identify and address ethical and social issues. However, they need to be understood as part of a broader socio-technical ecosystem of AI. For ALTAI and related approaches to be useful in bio-medical research, they should be interpreted from a systems theory perspective which allows for their integration into the rich set of tools, legislation and approaches. The paper argues that ex-ante impact assessments have the best chance of being successful if seen applied in conjunction with other approaches in the context of the overall AI ecosystem.