diff --git a/scoping_review.qmd b/scoping_review.qmd
index af16167..4db9a42 100644
--- a/scoping_review.qmd
+++ b/scoping_review.qmd
@@ -418,6 +418,7 @@ print(f"""
 ```{{mermaid}}
 %%| label: fig-prisma
 %%| fig-cap: "Sample sorting process through identification and screening"
+%%| fig-width: 6
 flowchart TD;
     search_db["Records identified through database searching (n=1643)"] --> starting_sample;
     search_prev["Records identified through other sources (n=753)"] --> starting_sample["Starting sample (n=2396)"];
@@ -557,7 +558,6 @@ Should they point towards gaps (or over-optimization) of sepcific areas of inter
 ```{python}
 #| label: fig-intervention-types
 #| fig-cap: Predominant type of intervention
-#| column: page
 
 interv_type_df = (
     bib_df["zot_keywords"]
@@ -569,7 +569,7 @@ interv_type_df = (
 
 sort_order = interv_type_df["intervention type"].value_counts(ascending=False).index
 fig = plt.figure()
-fig.set_size_inches(12, 4)
+fig.set_size_inches(6, 3)
 ax = sns.countplot(interv_type_df, x="intervention type", order=sort_order)
 plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
          rotation_mode="anchor")
@@ -581,7 +581,6 @@ plt.show()
 ```{python}
 #| label: fig-inequality-types
 #| fig-cap: Types of inequality analyzed
-#| column: page
 
 inequ_type_df = (
     bib_df["zot_keywords"]
@@ -593,7 +592,7 @@ inequ_type_df = (
 
 sort_order = inequ_type_df["inequality type"].value_counts(ascending=False).index
 fig = plt.figure()
-fig.set_size_inches(12, 4)
+fig.set_size_inches(6, 3)
 ax = sns.countplot(inequ_type_df, x="inequality type", order=sort_order)
 plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
          rotation_mode="anchor")