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I Horoi - One of the best experts on this subject based on the ideXlab platform.
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A Step-By-Step Approach to improve data quality when using commercial business lists to characterize retail food environments
BMC Research Notes, 2017Co-Authors: K K Jones, S A Matthews, E Tarlov, L M Powell, Shannon N. Zenk, I HoroiAbstract:Abstract Background Food environment characterization in health studies often requires data on the location of food stores and restaurants. While commercial business lists are commonly used as data sources for such studies, current literature provides little guidance on how to use validation study results to make decisions on which commercial business list to use and how to maximize the accuracy of those lists. Using data from a retrospective cohort study [Weight And Veterans’ Environments Study (WAVES)], we (a) explain how validity and bias information from existing validation studies (count accuracy, classification accuracy, locational accuracy, as well as potential bias by neighborhood racial/ethnic composition, economic characteristics, and urbanicity) were used to determine which commercial business listing to purchase for retail food outlet data and (b) describe the methods used to maximize the quality of the data and results of this Approach. Methods We developed data improvement methods based on existing validation studies. These methods included purchasing records from commercial business lists (InfoUSA and Dun and Bradstreet) based on store/restaurant names as well as standard industrial classification (SIC) codes, reclassifying records by store type, improving geographic accuracy of records, and deduplicating records. We examined the impact of these procedures on food outlet counts in US census tracts. Results After cleaning and deduplicating, our strategy resulted in a 17.5% reduction in the count of food stores that were valid from those purchased from InfoUSA and 5.6% reduction in valid counts of restaurants purchased from Dun and Bradstreet. Locational accuracy was improved for 7.5% of records by applying street addresses of subsequent years to records with post-office (PO) box addresses. In total, up to 83% of US census tracts annually experienced a change (either positive or negative) in the count of retail food outlets between the initial purchase and the final dataset. Discussion Our study provides a Step-By-Step Approach to purchase and process business list data obtained from commercial vendors. The Approach can be followed by studies of any size, including those with datasets too large to process each record by hand and will promote consistency in characterization of the retail food environment across studies.
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A Step-By-Step Approach to improve data quality when using commercial business lists to characterize retail food environments
BMC Research Notes, 2017Co-Authors: K K Jones, S A Matthews, E Tarlov, L M Powell, Shannon N. Zenk, I HoroiAbstract:BACKGROUND: Food environment characterization in health studies often requires data on the location of food stores and restaurants. While commercial business lists are commonly used as data sources for such studies, current literature provides little guidance on how to use validation study results to make decisions on which commercial business list to use and how to maximize the accuracy of those lists. Using data from a retrospective cohort study [Weight And Veterans' Environments Study (WAVES)], we (a) explain how validity and bias information from existing validation studies (count accuracy, classification accuracy, locational accuracy, as well as potential bias by neighborhood racial/ethnic composition, economic characteristics, and urbanicity) were used to determine which commercial business listing to purchase for retail food outlet data and (b) describe the methods used to maximize the quality of the data and results of this Approach.\nMETHODS: We developed data improvement methods based on existing validation studies. These methods included purchasing records from commercial business lists (InfoUSA and Dun and Bradstreet) based on store/restaurant names as well as standard industrial classification (SIC) codes, reclassifying records by store type, improving geographic accuracy of records, and deduplicating records. We examined the impact of these procedures on food outlet counts in US census tracts.\nRESULTS: After cleaning and deduplicating, our strategy resulted in a 17.5% reduction in the count of food stores that were valid from those purchased from InfoUSA and 5.6% reduction in valid counts of restaurants purchased from Dun and Bradstreet. Locational accuracy was improved for 7.5% of records by applying street addresses of subsequent years to records with post-office (PO) box addresses. In total, up to 83% of US census tracts annually experienced a change (either positive or negative) in the count of retail food outlets between the initial purchase and the final dataset.\nDISCUSSION: Our study provides a Step-By-Step Approach to purchase and process business list data obtained from commercial vendors. The Approach can be followed by studies of any size, including those with datasets too large to process each record by hand and will promote consistency in characterization of the retail food environment across studies.
K K Jones - One of the best experts on this subject based on the ideXlab platform.
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A Step-By-Step Approach to improve data quality when using commercial business lists to characterize retail food environments
BMC Research Notes, 2017Co-Authors: K K Jones, S A Matthews, E Tarlov, L M Powell, Shannon N. Zenk, I HoroiAbstract:Abstract Background Food environment characterization in health studies often requires data on the location of food stores and restaurants. While commercial business lists are commonly used as data sources for such studies, current literature provides little guidance on how to use validation study results to make decisions on which commercial business list to use and how to maximize the accuracy of those lists. Using data from a retrospective cohort study [Weight And Veterans’ Environments Study (WAVES)], we (a) explain how validity and bias information from existing validation studies (count accuracy, classification accuracy, locational accuracy, as well as potential bias by neighborhood racial/ethnic composition, economic characteristics, and urbanicity) were used to determine which commercial business listing to purchase for retail food outlet data and (b) describe the methods used to maximize the quality of the data and results of this Approach. Methods We developed data improvement methods based on existing validation studies. These methods included purchasing records from commercial business lists (InfoUSA and Dun and Bradstreet) based on store/restaurant names as well as standard industrial classification (SIC) codes, reclassifying records by store type, improving geographic accuracy of records, and deduplicating records. We examined the impact of these procedures on food outlet counts in US census tracts. Results After cleaning and deduplicating, our strategy resulted in a 17.5% reduction in the count of food stores that were valid from those purchased from InfoUSA and 5.6% reduction in valid counts of restaurants purchased from Dun and Bradstreet. Locational accuracy was improved for 7.5% of records by applying street addresses of subsequent years to records with post-office (PO) box addresses. In total, up to 83% of US census tracts annually experienced a change (either positive or negative) in the count of retail food outlets between the initial purchase and the final dataset. Discussion Our study provides a Step-By-Step Approach to purchase and process business list data obtained from commercial vendors. The Approach can be followed by studies of any size, including those with datasets too large to process each record by hand and will promote consistency in characterization of the retail food environment across studies.
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A Step-By-Step Approach to improve data quality when using commercial business lists to characterize retail food environments
BMC Research Notes, 2017Co-Authors: K K Jones, S A Matthews, E Tarlov, L M Powell, Shannon N. Zenk, I HoroiAbstract:BACKGROUND: Food environment characterization in health studies often requires data on the location of food stores and restaurants. While commercial business lists are commonly used as data sources for such studies, current literature provides little guidance on how to use validation study results to make decisions on which commercial business list to use and how to maximize the accuracy of those lists. Using data from a retrospective cohort study [Weight And Veterans' Environments Study (WAVES)], we (a) explain how validity and bias information from existing validation studies (count accuracy, classification accuracy, locational accuracy, as well as potential bias by neighborhood racial/ethnic composition, economic characteristics, and urbanicity) were used to determine which commercial business listing to purchase for retail food outlet data and (b) describe the methods used to maximize the quality of the data and results of this Approach.\nMETHODS: We developed data improvement methods based on existing validation studies. These methods included purchasing records from commercial business lists (InfoUSA and Dun and Bradstreet) based on store/restaurant names as well as standard industrial classification (SIC) codes, reclassifying records by store type, improving geographic accuracy of records, and deduplicating records. We examined the impact of these procedures on food outlet counts in US census tracts.\nRESULTS: After cleaning and deduplicating, our strategy resulted in a 17.5% reduction in the count of food stores that were valid from those purchased from InfoUSA and 5.6% reduction in valid counts of restaurants purchased from Dun and Bradstreet. Locational accuracy was improved for 7.5% of records by applying street addresses of subsequent years to records with post-office (PO) box addresses. In total, up to 83% of US census tracts annually experienced a change (either positive or negative) in the count of retail food outlets between the initial purchase and the final dataset.\nDISCUSSION: Our study provides a Step-By-Step Approach to purchase and process business list data obtained from commercial vendors. The Approach can be followed by studies of any size, including those with datasets too large to process each record by hand and will promote consistency in characterization of the retail food environment across studies.
Frans J. Meijman - One of the best experts on this subject based on the ideXlab platform.
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A Step-By-Step Approach for science communication practitioners: a design perspective.
2012Co-Authors: Mauritius C M Van De Sanden, Frans J. MeijmanAbstract:Science communication processes are complex and uncertain. Designing and managing these processes using a Step-By-Step Approach, allows those with science communication responsibility to manoeuvre between moral or normative issues, practical experiences, empirical data and theoretical foundations. The tool described in this study is an evidence-based questionnaire, tested in practice for feasibility. The key element of this decision aid is a challenge to the science communication practitioners to reflect on their attitudes, knowledge, reasoning and decision-making in a Step-By-Step manner to question the aim, function and impact of each issue and attendant communication process or strategy. This Approach eventually leads to more professional science communication processes by systematic design. The Design-Based Research (DBR) derived from science education and applied in this study, may form a new methodology for further exploration of the gap between theory and practice in science communication and. Practitioners, scholars, and researchers all participate actively in DBR. 1 write in Nature Biotechnology that science communication becomes increasingly complex due to science and technology's development and intricate relationship with society. Science-related controversies in society typically involve clashes of values and beliefs. These controversies are due not only to deficits of scientific understanding, but also of social capital. 2 The deficits not only increase the complexity of the controversies, but the uncertainty of processes of science communication as well. The medical field teaches us that, particularly in the public arena, mono-causal explanations and simplistic solutions ignore the complexity of the relationships between individual health, care and public health. Values, norms and conventions in these three domains may be discordant. 3 Therefore, for the practice of science communication, we consider creating balances within the individual and between individuals, society and the scientific and professional domains as a basic assumption. However, the gap between science communication theory and practice is a deficit of the science communication domain itself. At the PCST 4 conferences where practitioners, scholars and researchers gather from all over the world, one recognizes a wide field of practice and a much smaller field of theory that in practice is rarely integrated. This problem is also often mentioned in science communication textbooks. 5,6,7 Moreover, practice is multifaceted ranging from well equipped, experienced or at least full- time information officers, science writers, web designers or science journalists, to practitioners, managers or researchers who are only occasionally involved in science communication. This study examines a science communication officer working in an academic institute for gene technology and society, who needs to communicate about new developments in predictive DNA-diagnostics. How should he prepare for various meetings with disparate audiences? To overcome the gap between theory and practice while addressing various people involved in or with science communication, we argue that a systematic Approach to facilitate the design perspective of science communication professionals might be helpful. This design perspective allows the professional to cope with a science communication problem efficiently and effectively, while taking into account the aforementioned contextual constructs and variables; thus leading to an optimised science communication process. How can this Approach prove to be insightful and manageable for the science communication practitioner looking for a profound and sustainable communication process? How can science
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A Step-By-Step Approach for science communication practitioners: a design perspective
Journal of Science Communication, 2012Co-Authors: Mauritius C M Van De Sanden, Frans J. MeijmanAbstract:Science communication processes are complex and uncertain. Designing and managing these processes using a Step-By-Step Approach, allows those with science communication responsibility to manoeuvre between moral or normative issues, practical experiences, empirical data and theoretical foundations. The tool described in this study is an evidence-based questionnaire, tested in practice for feasibility. The key element of this decision aid is a challenge to the science communication practitioners to reflect on their attitudes, knowledge, reasoning and decision-making in a Step-By-Step manner to question the aim, function and impact of each issue and attendant communication process or strategy. This Approach eventually leads to more professional science communication processes by systematic design. The Design-Based Research (DBR) derived from science education and applied in this study, may form a new methodology for further exploration of the gap between theory and practice in science communication and. Practitioners, scholars, and researchers all participate actively in DBR.
Daniela Colombini - One of the best experts on this subject based on the ideXlab platform.
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IEA/WHO toolkit for WMSDs prevention: criteria and practical tools for a step by step Approach.
Work (Reading Mass.), 2020Co-Authors: Emanuela Occhipinti, Daniela ColombiniAbstract:When studying WMSDs, several determinants and their interrelationship are considered as relevant. Hence the necessity of an "holistic" Approach to prevention, especially when preparing technical rules and strategic plans. There is a strong request, from OSH agencies and operators, for developing "simple" tools for risk assessment and management. In this context WHO asked IEA to develop a "Toolkit for WMSD prevention". The paper presents one of the main contribution to this WHO project, focused on selecting tools at different level for hazard identification, risk estimation and management. Proposals are based on two essential criteria: Acting on a Step-By-Step Approach; Taking into account the presence of multiple influencing factors. The proposals consider: A Basic Step devoted to hazard identification by operative "key-enter" questions, that can be operated also by non-experts. A First Step, (quick assessment), for identifying 3 possible conditions: acceptable; high risk present; more detailed analysis (via tools presented at second step) necessary. This step can be operated by non-experts with only some specific training. A Second Step, where recognized (i.e. from international standards or guidelines) tools for risk estimation are used. This step can be operated only by persons with some specific training.
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A toolkit for the analysis of biomechanical overload and prevention of WMSDs: Criteria, procedures and tool selection in a Step-By-Step Approach
International Journal of Industrial Ergonomics, 2016Co-Authors: Emanuela Occhipinti, Daniela ColombiniAbstract:Background: When studying work related musculoskeletal disorders (WMSDs), various factors (mechanical, organizational, psychophysical, individual) and their interrelationships have been considered to be important in general models for epidemiologic surveys and risk assessment and management. Hence the need for a "holistic" Approach towards MSD prevention. On the other hand, considering the widespread presence of these factors and of WMSDs in many work places located in both developed and developing countries, there is a strong demand from OSH agencies and operators for "simple" risk assessment and management tools that can also be used by non-experts. Objectives: This paper is one of the main contributions towards a WHO/IEA project for developing a "Toolkit for WMSD prevention" by the TC on MSD of the IEA. The paper focuses on selecting tools at different levels for hazard identification, risk estimation and management. The proposals were primarily developed in this context but they also derive from other converging issues such as the ISO TR 12295 - published in 2014. Methods and criteria: Proposals are based on two essential criteria: 1) adoption of a Step-By-Step Approach starting with basic tools and moving to more complex tools only when necessary; 2) factoring in complexity and the presence of multiple influencing factors at every step (although with different degrees of in-depth analysis). Results: The proposals include: Step one: identification of preliminary occupational hazards and priority setting via "key-enter" questions (at this step, all potential hazards affecting WMSDs should be considered). Step two: identification of risk factors for WMSDs, consisting of a "quick assessment" and substantially aimed at identifying three possible conditions: acceptable/no consequences; critical/redesign urgently needed; more detailed analysis required. Step three: recognized tools for estimating risk (of WMSDs) are used depending on the outcomes of step two. Examples of such tools include "adaptations" of the Revised NIOSH Lifting Equation, Liberty Mutual Psychophysical Tables, OCRA Checklist, etc. These tools should adequately cover most of the influencing factors. Relevance to industry: The use of a Step-By-Step Approach and validated risk estimation tools, in accordance with international standards, makes it possible to tackle the challenge of simplifying complexity in the assessment of biomechanical overload conditions and in the prevention of WMSDs in enterprises of all sizes, small businesses, agriculture, and in developing countries.
S A Matthews - One of the best experts on this subject based on the ideXlab platform.
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A Step-By-Step Approach to improve data quality when using commercial business lists to characterize retail food environments
BMC Research Notes, 2017Co-Authors: K K Jones, S A Matthews, E Tarlov, L M Powell, Shannon N. Zenk, I HoroiAbstract:Abstract Background Food environment characterization in health studies often requires data on the location of food stores and restaurants. While commercial business lists are commonly used as data sources for such studies, current literature provides little guidance on how to use validation study results to make decisions on which commercial business list to use and how to maximize the accuracy of those lists. Using data from a retrospective cohort study [Weight And Veterans’ Environments Study (WAVES)], we (a) explain how validity and bias information from existing validation studies (count accuracy, classification accuracy, locational accuracy, as well as potential bias by neighborhood racial/ethnic composition, economic characteristics, and urbanicity) were used to determine which commercial business listing to purchase for retail food outlet data and (b) describe the methods used to maximize the quality of the data and results of this Approach. Methods We developed data improvement methods based on existing validation studies. These methods included purchasing records from commercial business lists (InfoUSA and Dun and Bradstreet) based on store/restaurant names as well as standard industrial classification (SIC) codes, reclassifying records by store type, improving geographic accuracy of records, and deduplicating records. We examined the impact of these procedures on food outlet counts in US census tracts. Results After cleaning and deduplicating, our strategy resulted in a 17.5% reduction in the count of food stores that were valid from those purchased from InfoUSA and 5.6% reduction in valid counts of restaurants purchased from Dun and Bradstreet. Locational accuracy was improved for 7.5% of records by applying street addresses of subsequent years to records with post-office (PO) box addresses. In total, up to 83% of US census tracts annually experienced a change (either positive or negative) in the count of retail food outlets between the initial purchase and the final dataset. Discussion Our study provides a Step-By-Step Approach to purchase and process business list data obtained from commercial vendors. The Approach can be followed by studies of any size, including those with datasets too large to process each record by hand and will promote consistency in characterization of the retail food environment across studies.
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A Step-By-Step Approach to improve data quality when using commercial business lists to characterize retail food environments
BMC Research Notes, 2017Co-Authors: K K Jones, S A Matthews, E Tarlov, L M Powell, Shannon N. Zenk, I HoroiAbstract:BACKGROUND: Food environment characterization in health studies often requires data on the location of food stores and restaurants. While commercial business lists are commonly used as data sources for such studies, current literature provides little guidance on how to use validation study results to make decisions on which commercial business list to use and how to maximize the accuracy of those lists. Using data from a retrospective cohort study [Weight And Veterans' Environments Study (WAVES)], we (a) explain how validity and bias information from existing validation studies (count accuracy, classification accuracy, locational accuracy, as well as potential bias by neighborhood racial/ethnic composition, economic characteristics, and urbanicity) were used to determine which commercial business listing to purchase for retail food outlet data and (b) describe the methods used to maximize the quality of the data and results of this Approach.\nMETHODS: We developed data improvement methods based on existing validation studies. These methods included purchasing records from commercial business lists (InfoUSA and Dun and Bradstreet) based on store/restaurant names as well as standard industrial classification (SIC) codes, reclassifying records by store type, improving geographic accuracy of records, and deduplicating records. We examined the impact of these procedures on food outlet counts in US census tracts.\nRESULTS: After cleaning and deduplicating, our strategy resulted in a 17.5% reduction in the count of food stores that were valid from those purchased from InfoUSA and 5.6% reduction in valid counts of restaurants purchased from Dun and Bradstreet. Locational accuracy was improved for 7.5% of records by applying street addresses of subsequent years to records with post-office (PO) box addresses. In total, up to 83% of US census tracts annually experienced a change (either positive or negative) in the count of retail food outlets between the initial purchase and the final dataset.\nDISCUSSION: Our study provides a Step-By-Step Approach to purchase and process business list data obtained from commercial vendors. The Approach can be followed by studies of any size, including those with datasets too large to process each record by hand and will promote consistency in characterization of the retail food environment across studies.
