diff --git a/scoping_review.qmd b/scoping_review.qmd
index 1ec5aa6..bb14c97 100644
--- a/scoping_review.qmd
+++ b/scoping_review.qmd
@@ -151,6 +151,8 @@ The ILO has a policy approach to reducing inequalities in the world of work segm
 Each of these areas in turn rests on a variety of more specific emphases which further describe the potential implemented policy measures.
 An exemplary typology of general policy area, related specified policy focus and related focus if any can be found in @tbl-policy-areas.
 
+::: {#tbl-policy-areas}
+
 | area of policy           | focus                                              | related                          |
 |-------------------------:|----------------------------------------------------|----------------------------------|
 | employment creation      | pro-employment framework                           |                                  |
@@ -189,10 +191,12 @@ An exemplary typology of general policy area, related specified policy focus and
 |                          |                                                    | adequate social protection       |
 |                          |                                                    | sustainable social protection    |
 
-: ILO focus areas for inequality reduction {#tbl-policy-areas}
-
 Source: Authors' elaboration based on ILO [-@ILO2022b].
 
+ILO focus areas for inequality reduction
+
+:::
+
 ## Inequalities in the world of work
 
 Inequalities in the world of work have to be fundamentally conceptualized along two axes: On the one hand, vertical inequality captures the "income inequality between all households in a country" [@ILO2021].
@@ -364,13 +368,18 @@ To identify potential studies and create an initial sample, relevant terms for t
 Identified terms comprising the world of work can be found in @tbl-wow-terms,
 with the search query requiring a term from the general column and one other column.
 
+::: {#tbl-wow-terms}
+
 ```{python}
 #| label: tbl-wow-terms
-#| tbl-cap: World of work term cluster
 terms_wow = pd.read_csv("02-data/supplementary/terms_wow.csv")
 md(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
 ```
 
+World of work term cluster
+
+:::
+
 The world of work cluster, like the inequality and policy intervention clusters below, is made up of a general signifier (such as "work", "inequality" or "intervention") which has to be labelled in a study to form part of the sample,
 as well as any additional terms looking into one or multiple specific dimensions or categories of these signifiers (such as "domestic" work, "gender" inequality, "maternity leave" intervention).
 At least one general term and at least one additional term have to be mentioned by a study to be identified for the initial sample pool.
@@ -380,15 +389,20 @@ Where terms have been identified from previous reviews outside the introduced IL
 there source has been included in the table.
 For the database query, a single term from the general category is required to be included in addition to one term from *any* of the remaining categories.
 
+::: {#tbl-intervention-terms}
+
 ```{python}
 #| label: tbl-intervention-terms
-#| tbl-cap: Policy intervention term cluster
 terms_policy = pd.read_csv("02-data/supplementary/terms_policy.csv")
 # different headers to include 'social norms'
 headers = ["General", "Institutional", "Structural", "Agency & social norms"]
 md(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
 ```
 
+Policy intervention term cluster
+
+:::
+
 Lastly, the inequality cluster is once again made up of a general term describing inequality which has to form part of the query results, as well as at least one term describing a specific vertical or horizontal inequality,
 as seen in @tbl-inequality-terms.
 
@@ -414,14 +428,21 @@ 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.
 
+::: {#tbl-inclusion-criteria}
+
 ```{python}
 #| label: tbl-inclusion-criteria
-#| tbl-cap: 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="keys", tablefmt="grid"))
 ```
 
+Source: Author's elaboration
+
+Study inclusion and exclusion scoping criteria
+
+:::
+
 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.
 To that end, a ‘country::’, ‘region::’ and ‘income::’ are used to disambiguate between the respective characteristics, such as ‘region::LAC’ for Latin America and the Caribbean, ‘region::SSA’ for Sub-Saharan Africa; as well as for example ‘income::low-middle’, ‘income::upper-middle’ or ‘income::high’.