Анализ модельного риска использования технологии мультипликаторов при оценке акций российских компаний (Assessment of Model Risk of Technology Use Multipliers in the Valuation of Shares of Russian Companies)

2020 ◽  
Author(s):  
Vigen Babkenovich Minasyan ◽  
Daria Ivko
Keyword(s):  
Technology Use ◽  
Model Risk

Aging and assistive technology use

1997 ◽  
Vol 6 (1-2) ◽  
pp. 63-75 ◽  
Author(s):  
William C. Mann
Keyword(s):  
Assistive Technology ◽  
Technology Use

Computer Technology Use and Attitudes Among Older Adults

2013 ◽  
Author(s):  
Virginia Wadley ◽  
Rachel Benz ◽  
Martha Frankel ◽  
David Ball ◽  
Daniel Roenker
Keyword(s):  
Older Adults ◽  
Computer Technology ◽  
Technology Use

Survey of psychologists’ telebehavioral health practices: Technology use, ethical issues, and training needs.

2018 ◽  
Vol 49 (3) ◽  
pp. 205-219 ◽  
Author(s):  
Robert L. Glueckauf ◽  
Marlene M. Maheu ◽  
Kenneth P. Drude ◽  
Brittny A. Wells ◽  
Yuxia Wang ◽  
...  
Keyword(s):  
Technology Use ◽  
Ethical Issues ◽  
Training Needs ◽  
Health Practices ◽  
Telebehavioral Health ◽  
And Training

A Decade of Disparities in Diabetes Technology Use and HbA1c in Pediatric Type 1 Diabetes: A Trans-Atlantic Comparison

2020 ◽  
Author(s):  
Ananta Addala ◽  
Marie Auzanneau ◽  
Kellee Miller ◽  
Werner Maier ◽  
Nicole Foster ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Technology Use ◽  
Hemoglobin A1c ◽  
Diabetes Technology ◽  
Design And Methods ◽  
Relationship Of ◽  
The Relationship ◽  
Pediatric Type 1 Diabetes

<b>Objective:</b> As diabetes technology use in youth increases worldwide, inequalities in access may exacerbate disparities in hemoglobin A1c (HbA1c). We hypothesized an increasing gap in diabetes technology use by socioeconomic status (SES) would be associated with increased HbA1c disparities. <p> </p> <p><b>Research Design and Methods: </b>Participants aged <18 years with diabetes duration ≥1 year in the Type 1 Diabetes Exchange (T1DX, US, n=16,457) and Diabetes Prospective Follow-up (DPV, Germany, n=39,836) registries were categorized into lowest (Q1) to highest (Q5) SES quintiles. Multiple regression analyses compared the relationship of SES quintiles with diabetes technology use and HbA1c from 2010-2012 and 2016-2018. </p> <p> </p> <p><b>Results: </b>HbA1c was higher in participants with lower SES (in 2010-2012 & 2016-2018, respectively: 8.0% & 7.8% in Q1 and 7.6% & 7.5% in Q5 for DPV; and 9.0% & 9.3% in Q1 and 7.8% & 8.0% in Q5 for T1DX). For DPV, the association between SES and HbA1c did not change between the two time periods, whereas for T1DX, disparities in HbA1c by SES increased significantly (p<0.001). After adjusting for technology use, results for DPV did not change whereas the increase in T1DX was no longer significant.</p> <p> </p> <p><b>Conclusions: </b>Although causal conclusions cannot be drawn, diabetes technology use is lowest and HbA1c is highest in those of the lowest SES quintile in the T1DX and this difference for HbA1c broadened in the last decade. Associations of SES with technology use and HbA1c were weaker in the DPV registry. </p>

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