diff --git a/01-codechunks/_prep-data.py b/01-codechunks/_prep-data.py
index bfb1c02..0ad0d61 100644
--- a/01-codechunks/_prep-data.py
+++ b/01-codechunks/_prep-data.py
@@ -2,7 +2,7 @@ from pathlib import Path
 import os
 import re
 ## standard imports
-from IPython.core.display import Markdown as md
+from IPython.display import display, Markdown, HTML
 import numpy as np
 import pandas as pd
 from matplotlib import pyplot as plt
diff --git a/article.qmd b/article.qmd
index 5035f70..933f43d 100644
--- a/article.qmd
+++ b/article.qmd
@@ -90,7 +90,7 @@ with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
 
 ```{python}
 inclusion_criteria = pd.read_csv("02-data/supplementary/inclusion-criteria.tsv", sep="\t")
-md(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
+Markdown(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
 ```
 
 Source: Author's elaboration
@@ -211,7 +211,7 @@ def strength_for(val):
 
 findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
 
-outp = md(
+outp = Markdown(
     tabulate(
         validity.add_to_findings(
             findings_institutional, df_by_intervention, study_strength_bins
@@ -680,7 +680,7 @@ Another reason could be the actual implementation of different policy programmes
 
 ```{python}
 terms_wow = pd.read_csv("02-data/supplementary/terms_wow.csv")
-md(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
+Markdown(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
 ```
 
 World of work term cluster
@@ -693,7 +693,7 @@ World of work 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"))
+Markdown(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
 ```
 
 Policy intervention term cluster
@@ -704,7 +704,7 @@ Policy intervention term cluster
 
 ```{python}
 terms_inequality = pd.read_csv("02-data/supplementary/terms_inequality.csv")
-md(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
+Markdown(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
 ```
 
 Inequality term cluster
diff --git a/scoping_review.qmd b/scoping_review.qmd
index 46ece2f..4850557 100644
--- a/scoping_review.qmd
+++ b/scoping_review.qmd
@@ -290,7 +290,7 @@ with the search query requiring a term from the general column and one other col
 #| 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"))
+Markdown(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
 ```
 
 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,
@@ -308,7 +308,7 @@ For the database query, a single term from the general category is required to b
 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"))
+Markdown(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
 ```
 
 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,
@@ -318,7 +318,7 @@ as seen in @tbl-inequality-terms.
 #| label: tbl-inequality-terms
 #| tbl-cap: Inequality term cluster
 terms_inequality = pd.read_csv("02-data/supplementary/terms_inequality.csv")
-md(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
+Markdown(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
 ```
 
 A general as well as category-specific term from each cluster will be required, using a intersection merge (Boolean 'AND'),
@@ -342,7 +342,7 @@ with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
 #| label: 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"))
+Markdown(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
 ```
 
 Source: Author's elaboration
@@ -543,7 +543,7 @@ def strength_for(val):
 findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
 fd_df = validity.add_to_findings(findings_institutional, by_intervention, study_strength_bins)
 
-md(tabulate(fd_df[["area of policy",  "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
+Markdown(tabulate(fd_df[["area of policy",  "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
 ```
 
 Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.
@@ -775,7 +775,7 @@ from src.model import validity
 findings_structural = pd.read_csv("02-data/supplementary/findings-structural.csv")
 fd_df = validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)
 
-md(tabulate(fd_df[["area of policy",  "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
+Markdown(tabulate(fd_df[["area of policy",  "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
 ```
 
 Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.
@@ -1012,7 +1012,7 @@ from src.model import validity
 findings_agency = pd.read_csv("02-data/supplementary/findings-agency.csv")
 fd_df = validity.add_to_findings(findings_agency, by_intervention, study_strength_bins)
 
-md(tabulate(fd_df[["area of policy",  "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
+Markdown(tabulate(fd_df[["area of policy",  "internal_validity", "external_validity", "findings", "channels"]].fillna(""), showindex=False, headers=["area of policy", "internal strength", "external strength", "main findings", "channels"], tablefmt="grid"))
 ```
 
 Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.