Understanding Human Enactment of Technology on Digital Labor Platforms

This study examines gig workers’ interactions with digital platforms to reveal how workers see technology in relation to their conduct of work. Gig workers are paid labors who find short-term tasks or projects through a digital labor platform (DLP) that connects clients and workers. Workers are intertwined with technologies in gig work. On DLPs such as Uber, tensions arise between humans and algorithmic management. Yet, our understanding of worker perceptions of DLP technologies remains limited. This study focuses on place-based gig work of delivery and grocery shopping (e.g., Instacart, Postmates) and draws upon sociomateriality research to reveal workers’ perceptions. Analysis of worker narratives revealed three themes related to worker enactments of technology on DLPs (affording, constraining, and seeking alternatives) and two co-existing, contradictory identities of technology (aid vs. obstacle). The dual relations suggest new dimensions of sociomateriality on DLPs and offer practical implications on the digitalization of work

Diagnostic accuracy and outcomes of digital breast tomosynthesis (DBT) compared to digital mammography (DM) in identifying breast cancer in women at increased risk of breast cancer due to symptoms and/or family history.

Aims: 1. To examine the diagnostic accuracy and outcomes of DBT, in women with symptoms or signs of breast cancer and/or family history of breast cancer, in comparison with DM. 2. To examine the diagnostic accuracy and outcomes of DBT in the subgroup of women with dense breasts who have symptoms or signs of breast cancer and/or family history of breast cancer, in comparison with DM. 3. To examine the additional diagnostic value of breast ultrasound, used in addition to either DBT or DM in the above-described populations including the subgroup with dense breasts

Abordagem teórico-reflexiva da estratégia de ensino 4E X 2 e sua interface com a Teoria Metacognitiva

Ensaio teórico reflexivo sobre a abordagem metacognitiva de uma estratégia de ensino. 4E X 2 corresponde a uma proposta de instrução por investigação criada por pesquisadores da Clemson University College Education nos EUA na qual 4E significa: Engajar, Explorar, Explicar e Estender e o X2, significa Avaliar e Refletir. O objetivo deste trabalho é apresentar e descrever a 4E X 2 e relacionar seus procedimentos e constructos com a teoria e estratégias de ensino metacognitivo. A descrição detalhada dos procedimentos de aplicação da estratégia fornece subsídios para sua utilização por professores do ensino básico, enquanto sua análise explicita a conexão com a teoria metacognitiva e estratégias de ensino metacognitivas. Observamos aspectos positivos na forma pragmática como os templates e protocolos são apresentados, verificamos interfaces entre a estratégia e as componentes conhecimento do conhecimento e do controle executivo autorregulador, assim como, os procedimentos de aplicação apresentam-se compatíveis com as definições das funções das estratégias de ensino metacognitivo. Este estudo reforça a importância da aproximação da produção do contexto acadêmico com o ambiente escolar, onde o principal objetivo sempre será a reprodução possível destas teorias na prática do dia a dia do professor

Granulocyte colony-stimulating factors for febrile neutropenia prophylaxis following chemotherapy: systematic review and meta-analysis

Background: Febrile neutropenia (FN) occurs following myelosuppressive chemotherapy and is associated with morbidity, mortality, costs, and chemotherapy reductions and delays. Granulocyte colony-stimulating factors (G-CSFs) stimulate neutrophil production and may reduce FN incidence when given prophylactically following chemotherapy. Methods: A systematic review and meta-analysis assessed the effectiveness of G-CSFs (pegfilgrastim, filgrastim or lenograstim) in reducing FN incidence in adults undergoing chemotherapy for solid tumours or lymphoma. G-CSFs were compared with no primary G-CSF prophylaxis and with one another. Nine databases were searched in December 2009. Meta-analysis used a random effects model due to heterogeneity. Results: Twenty studies compared primary G-CSF prophylaxis with no primary G-CSF prophylaxis: five studies of pegfilgrastim; ten of filgrastim; and five of lenograstim. All three G-CSFs significantly reduced FN incidence, with relative risks of 0.30 (95% CI: 0.14 to 0.65) for pegfilgrastim, 0.57 (95% CI: 0.48 to 0.69) for filgrastim, and 0.62 (95% CI: 0.44 to 0.88) for lenograstim. Overall, the relative risk of FN for any primary G-CSF prophylaxis versus no primary G-CSF prophylaxis was 0.51 (95% CI: 0.41 to 0.62). In terms of comparisons between different G-CSFs, five studies compared pegfilgrastim with filgrastim. FN incidence was significantly lower for pegfilgrastim than filgrastim, with a relative risk of 0.66 (95% CI: 0.44 to 0.98). Conclusions: Primary prophylaxis with G-CSFs significantly reduces FN incidence in adults undergoing chemotherapy for solid tumours or lymphoma. Pegfilgrastim reduces FN incidence to a significantly greater extent than filgrastim

Measuring the predictability of life outcomes with a scientific mass collaboration.

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences

Intervenciones de diseño participativo: apoyando redes de cuidado universitarias en tiempos de Covid

Peer reviewe

COVID-19: how many Australians might have died if we’d had an outbreak like that in England and Wales?

Using all-cause mortality data in England and Wales over the peak of the COVID-19 outbreak in March and April, we directly estimated the number of excess deaths that may have occurred if the outbreak in Australia had been of a similar extent to that in England and Wales

Mendelian Randomization in Cardiovascular Research: Establishing Causality When There Are Unmeasured Confounders

Mendelian randomization is an epidemiological approach to making causal inferences using observational data. It makes use of the natural randomization that occurs in the generation of an individual’s genetic makeup in a way that is analogous to the study design of a randomized controlled trial and uses instrumental variable analysis where the genetic variant(s) are the instrument (analogous to random allocation to treatment group in an randomized controlled trial). As with any instrumental variable, there are 3 assumptions that must be made about the genetic instrument: (1) it is associated (not necessarily causally) with the exposure (relevance condition); (2) it is associated with the outcome only through the exposure (exclusion restriction condition); and (3) it does not share a common cause with the outcome (ie, no confounders of the genetic instrument and outcome, independence condition). Using the example of type II diabetes and coronary artery disease, we demonstrate how the method may be used to investigate causality and discuss potential benefits and pitfalls. We conclude that although Mendelian randomization studies can usually not establish causality on their own, they may usefully contribute to the evidence base and increase our certainty about the effectiveness (or otherwise) of interventions to reduce cardiovascular disease

Testing decisions in the Pandemic: How do we use imperfect tests for COVID-19 (SARS-CoV-2) to make clinical decisions

This is the second article of a series on how to use diagnostic test accuracy evidence. In this article, we explain how diagnostic test accuracy estimates may be applied in clinical practice, using the example of coronavirus disease 2019 (COVID-19). Using clinical scenarios, we guide the reader through the process of applying estimates of diagnostic accuracy to change the pre-test probability of disease, and make clinical decisions about their patient