Image coding was performed using a coding protocol to guide content analysis [23 ]. Customised software enabled manual coding of each image. Marketing was defined as “any form of commercial communication or message that is designed to, or has the effect of, increasing recognition, appeal and/or consumption of particular products and services” ([24 ], p.9). A three-tiered framework was used to code each relevant image for setting, marketing medium and food product category, based on the WHO food marketing framework [9 ]. Key settings codes were home, school, food venues, recreation venues and other public spaces. Key marketing media codes were product packaging, signs, in-store marketing, print media, screen and merchandise.
MB, TC and four other health science students undertook the coding. A half day training workshop was held with all coders and coders were then given access to the dataset for a number of days to become familiar with it. Once coders felt comfortable, reliability testing was conducted, with each coder achieving 90% concurrence with model answers on a test dataset of 115 images before coding commenced. Coders were supervised by MS, MB and TC to ensure consistency. Uncertain codes were noted as such and checked by MB or TC.
All foods were classified as either recommended (core) or not recommended (non-core) to be marketed to children based on the WHO Regional Office for Europe Nutrient Profiling Model [9 ], with some modifications (e.g. a ‘fast food’ category was added which included all commercially prepared food products sold at quick service restaurants). All fast food was classified as not recommended to be marketed to children as it is typically high in saturated fat and sodium and low in fiber [25 ]. Marketing in convenience stores and supermarkets was too extensive to code individually and was therefore excluded from this analysis. Codes were only assigned to an image where 50% or more of a brand name or logo could be clearly seen by the coder. Individual images could be coded for multiple marketing media and product categories.
Further processing of the coded data included determining the number of marketing exposures for each unique exposure code (defined as the combination of setting, medium and product type for that code). A marketing exposure was defined as starting on the first instance of an image with a particular setting/medium/product code; subsequent images were counted as part of the same exposure. An exposure was considered to have ended when 30 s had elapsed since the last recorded code of that setting/medium/product code (defined using the image timestamps). Any subsequent code for that same combination after this 30 s limit was counted as the start of a new exposure sequence.
The number of exposures was summed for each unique exposure code by child; aggregate counts were determined for each child to estimate total exposures to core and non-core foods, and exposure by setting, medium, and product type. Cleaning and aggregation of coded data was completed in R version 3.2.3 (R Institute, Vienna).
MB, TC and four other health science students undertook the coding. A half day training workshop was held with all coders and coders were then given access to the dataset for a number of days to become familiar with it. Once coders felt comfortable, reliability testing was conducted, with each coder achieving 90% concurrence with model answers on a test dataset of 115 images before coding commenced. Coders were supervised by MS, MB and TC to ensure consistency. Uncertain codes were noted as such and checked by MB or TC.
All foods were classified as either recommended (core) or not recommended (non-core) to be marketed to children based on the WHO Regional Office for Europe Nutrient Profiling Model [9 ], with some modifications (e.g. a ‘fast food’ category was added which included all commercially prepared food products sold at quick service restaurants). All fast food was classified as not recommended to be marketed to children as it is typically high in saturated fat and sodium and low in fiber [25 ]. Marketing in convenience stores and supermarkets was too extensive to code individually and was therefore excluded from this analysis. Codes were only assigned to an image where 50% or more of a brand name or logo could be clearly seen by the coder. Individual images could be coded for multiple marketing media and product categories.
Further processing of the coded data included determining the number of marketing exposures for each unique exposure code (defined as the combination of setting, medium and product type for that code). A marketing exposure was defined as starting on the first instance of an image with a particular setting/medium/product code; subsequent images were counted as part of the same exposure. An exposure was considered to have ended when 30 s had elapsed since the last recorded code of that setting/medium/product code (defined using the image timestamps). Any subsequent code for that same combination after this 30 s limit was counted as the start of a new exposure sequence.
The number of exposures was summed for each unique exposure code by child; aggregate counts were determined for each child to estimate total exposures to core and non-core foods, and exposure by setting, medium, and product type. Cleaning and aggregation of coded data was completed in R version 3.2.3 (R Institute, Vienna).
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