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We used ATLAS.ti to categorise, code and analyse qualitative data including hundreds of pages of background documents such as national-level policy reports, minutes from meetings and speech transcripts from conferences and workshops (table 3) (figure 3).
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As data analysis progressed, the researchers met to compare emerging issues, resolve discrepancies and categorise codes into larger themes.
A particular issue is the way in which foods are categorised (coded) during data collection.
We extracted ethnicity from structured fields and categorised codes into 'Black African/Caribbean/other', 'White British/other', and 'Mixed, Asian or other'.
After categories were developed inductively, we asked whether the categories were strong enough to cover the content of the categorised codes.
Subsequent to this, the authors categorised similar codes into broader coding categories, with the final categories being those nominated by ≥ 5%% (e.g. the aforementioned codes were categorised into the broader coding category 'mental issue').
As with the summarising protocol, coders were to categorise Read codes as 'Significant'.
As indicator for physical (or "built") environment, a measure of the Ministry of Housing, Spatial Planning and the Environment was used, which categorises postal code areas as either "city centre", "city non-centre", "city green", "town centre", "rural area" or "work area" [ 32].
Data will be categorised and coded for a thematic analysis.
Reasons for use were categorised and coded concurrently with data entry.
Maternal drinking behaviours and attitudes were categorised, and coded in the same way.
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