The Experts below are selected from a list of 97209 Experts worldwide ranked by ideXlab platform
Thomas R Loveland - One of the best experts on this subject based on the ideXlab platform.
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quality control and assessment of Interpreter consistency of annual land cover reference data in an operational national monitoring program
Remote Sensing of Environment, 2020Co-Authors: Bruce W Pengra, Stephen V Stehman, Josephine A Horton, Daryn J Dockter, Todd A Schroeder, Zhiqiang Yang, Warren B Cohen, Sean P Healey, Thomas R LovelandAbstract:Abstract The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as “duplicate interpretations”). The annual land cover reference class labels for the 1984–2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among Interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as Interpreters gain experience over time? 4) Does Interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between Interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between Interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased Interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30-year period. Protocols to estimate and enhance Interpreter consistency are critical elements to document and ensure the quality of these reference data.
Warren B Cohen - One of the best experts on this subject based on the ideXlab platform.
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quality control and assessment of Interpreter consistency of annual land cover reference data in an operational national monitoring program
Remote Sensing of Environment, 2020Co-Authors: Bruce W Pengra, Stephen V Stehman, Josephine A Horton, Daryn J Dockter, Todd A Schroeder, Zhiqiang Yang, Warren B Cohen, Sean P Healey, Thomas R LovelandAbstract:Abstract The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as “duplicate interpretations”). The annual land cover reference class labels for the 1984–2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among Interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as Interpreters gain experience over time? 4) Does Interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between Interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between Interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased Interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30-year period. Protocols to estimate and enhance Interpreter consistency are critical elements to document and ensure the quality of these reference data.
Glenn Flores - One of the best experts on this subject based on the ideXlab platform.
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Access to hospital Interpreter services for limited English proficient patients in New Jersey: a statewide evaluation.
Journal of health care for the poor and underserved, 2008Co-Authors: Glenn Flores, Sylvia Torres, Linda Janet Holmes, Debbie Salas-lopez, Mara K Youdelman, Sandra C Tomany-kormanAbstract:Context/Objectives. We surveyed New Jersey (NJ) hospitals to assess current language services and identify policy recommendations on meeting limited English profi - ciency (LEP) patients' needs. Methods . Survey with 37 questions regarding hospital/patient features, Interpreter services, and resources/policies needed to provide quality Interpreter services. Results . Sixty-seven hospitals responded (55% response rate). Most NJ hospitals have no Interpreter services department, 80% provide no staff training on working with Interpreters, 31% lack multilingual signs, and 19% offer no written translation services. Only 3% of hospitals have full-time Interpreters, a ratio of 1 Interpreter:240,748 LEP NJ residents. Most hospitals stated third-party reimbursement for Interpreters would be ben- eficial, by reducing costs, adding Interpreters, meeting population growth, and improving communication. Conclusions . Most NJ hospitals have no full-time Interpreters, Interpreter services department, or staff training on working with Interpreters, and deficiencies exist in hospital signage and translation services. Most NJ hospitals stated third-party reimburse- ment for Interpreter services would be beneficial.
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the impact of medical Interpreter services on the quality of health care a systematic review
Medical Care Research and Review, 2005Co-Authors: Glenn FloresAbstract:Twenty-one million Americans are limited in English proficiency (LEP), but little is known about the effect of medical Interpreter services on health care quality. A systematic literature review was conducted on the impact of Interpreter services on quality of care. Five database searches yielded 2,640 citations and a final database of 36 articles, after applying exclusion criteria. Multiple studies document that quality of care is compromised when LEP patients need but do not get Interpreters. LEP patients’ quality of care is inferior, and more Interpreter errors occur with untrained ad hoc Interpreters. Inadequate Interpreter services can have serious consequences for patients with mental disorders. Trained professional Interpreters and bilingual health care providers positively affect LEP patients’ satisfaction, quality of care, and outcomes. Evidence suggests that optimal communication, patient satisfaction, and outcomes and the fewest Interpreter errors occur when LEP patients have access to trained professional Interpreters or bilingual providers.
Bruce W Pengra - One of the best experts on this subject based on the ideXlab platform.
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quality control and assessment of Interpreter consistency of annual land cover reference data in an operational national monitoring program
Remote Sensing of Environment, 2020Co-Authors: Bruce W Pengra, Stephen V Stehman, Josephine A Horton, Daryn J Dockter, Todd A Schroeder, Zhiqiang Yang, Warren B Cohen, Sean P Healey, Thomas R LovelandAbstract:Abstract The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as “duplicate interpretations”). The annual land cover reference class labels for the 1984–2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among Interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as Interpreters gain experience over time? 4) Does Interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between Interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between Interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased Interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30-year period. Protocols to estimate and enhance Interpreter consistency are critical elements to document and ensure the quality of these reference data.
Carlos A Camargo - One of the best experts on this subject based on the ideXlab platform.
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reevaluation of the effect of mandatory Interpreter legislation on use of professional Interpreters for ed patients with language barriers
Patient Education and Counseling, 2010Co-Authors: Adit A Ginde, Ashley F Sullivan, Blanka Corel, Alfredo J Caceres, Carlos A CamargoAbstract:Abstract Objective We sought to compare emergency department (ED) use of professional Interpreters in 2008 to previously reported 2002 data. Methods We surveyed consecutive adult patients for two 24-h periods at 4 Boston EDs in 2008. We used identical questions as in our 2002 study to assess English language barriers and to measure use and type of Interpreter for those with language barriers. Results We enrolled 498 patients (66% of eligible). Of these, 8% had a significant English language barrier, but any Interpreter was used for only 69% of these patients; the corresponding data for 2002 were 11% and 89%, respectively. In 2008, compared to 2002, professional Interpreter use was similar (18% vs. 15%; p = 0.70), but a friend or family member interpreted more often (59% vs. 24%; p Conclusion We found that use of professional Interpreters by Boston ED patients with language barriers remained low, despite publicity of the state mandatory Interpreter law. However, a majority were comfortable with a friend or family member serving as the Interpreter for the clinical encounter, a finding that may contribute to the limited usage of professional Interpreters.
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language barriers among patients in boston emergency departments use of medical Interpreters after passage of Interpreter legislation
Journal of Immigrant and Minority Health, 2009Co-Authors: Adit A Ginde, Sunday Clark, Carlos A CamargoAbstract:Background Since 2001, Massachusetts state law dictates that emergency department (ED) patients with limited English proficiency have the right to a professional Interpreter. Methods one year later, for two 24-h periods, we interviewed adult patients presenting to four Boston EDs. We assessed language barriers and compared this need with the observed use and type of Interpreter during the ED visit. Results We interviewed 530 patients (70% of eligible) and estimated that an Interpreter was needed for 60 (11%; 95% confidence interval, 7–12%) patients. The primary Interpreter for these clinical encounters was a physician (30%), friend or family member age ≥18 years (22%), hospital Interpreter services (15%), younger family member (11%), or other hospital staff (17%). Conclusions We found that 11% of ED patients had significant language barriers, but use of professional medical Interpreters remained low. One year after passage of legislation mandating access, use of professional medical Interpreters remained inadequate.
