From d9cd5fad1a3788247f0d50c777e4a1e9e53e6ee2 Mon Sep 17 00:00:00 2001 From: Marty Oehme Date: Wed, 6 Dec 2023 16:09:12 +0100 Subject: [PATCH] feat(script): Add dynamic inclusion criteria table In order to pursue single source of truth, we now explicitly integrate the inclusion criteria table from a tsv file in the data directory in the main manuscript. Other locations should also make use of that file to create their table. --- scoping_review.qmd | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/scoping_review.qmd b/scoping_review.qmd index 7e7e3e1..cf6f097 100644 --- a/scoping_review.qmd +++ b/scoping_review.qmd @@ -518,17 +518,13 @@ An overview of the respective criteria used for inclusion or exclusion can be fo It restricts studies to those that comprise primary research published after 2000, with a focus on the narrowing criteria specified in @tbl-inclusion-criteria. -| Parameter | Inclusion criteria | Exclusion criteria | -| --- | --- | --- | -| Time frame | study published in or after 2000 | study published before 2000 | -| Study type | primary research | opinion piece, editorial, commentary, news article, literature review | -| Study recency | most recent publication of study | gray literature superseded by white literature publication | -| Study focus | inequality or labour market outcomes as primary outcome (dependent variable) | neither inequality nor labour market outcomes as dependent variable | -| | policy measure or strategy as primary intervention (independent variable) | no policy measure/strategy as intervention or relationship unclear | -| | specifically relates to some dimension of world of work | exists outside world of work for both independent and dependent variables | -| | focus on dimension of inequality in analysis | no focus on mention of inequality in analysis | +```{python} +#| label: tbl-inclusion-criteria +#| fig-cap: Study inclusion and exclusion scoping criteria {#tbl-inclusion-criteria} -: Study inclusion and exclusion scoping criteria {#tbl-inclusion-criteria} +inclusion_criteria = pd.read_csv("02-data/supplementary/inclusion-criteria.tsv", sep="\t") +md(tabulate(inclusion_criteria, showindex=False, headers="firstrow", tablefmt="grid")) +``` To facilitate the screening process, with the help of 'Zotero' reference manager a system of keywords is used to tag individual studies in the sample with their reason for exclusion,such as ‘excluded::language’, ‘excluded::title’, ‘excluded::abstract’, and ‘excluded::superseded’. This keyword-based system is equally used to further categorize the sample studies that do not fall into exclusion criteria, based on primary country of analysis, world region, as well as income level classification.