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That was until I remembered that I had done their onboarding quiz a month ago and that their algorithm was actually just generating the items according to my answers.
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Therefore, to generate the items for inclusion in the questionnaire, we focused the questions specifically on any perceived differences in the threshold to suggest W/L and DNR by each pediatric intensivist.
To generate the items for inclusion in the questionnaire, we searched MEDLINE from 1996 to 2004 for articles on BD, followed by review of the relevant article reference lists.
The authors generated the items through literature review and discussions among tobacco researchers.
The authors generated the items from literature review and discussion with field workers.
A range of techniques was used to generate the items within a measure.
A group of experts used a consensus panel process to generate the items and validate the content.
The investigators generated the items from previously developed and validated instruments from the UK and refined the instrument for linguistic, content, and social acceptability.
To generate the items for the questionnaire, we searched Medline from 1980 to 2012 for articles about the methodology and translation of AR.
The authors used an iterative process of drafting, evaluation, and revision to generate the items, response options, instructions, and recall period for the instrument.
A combination of strategies was used to generate the items within these domains for the ORACLe interview and each is described in detail below.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com