professional/wow-inequalities
1
0
Fork 0
Code Issues Pull requests Packages Projects Releases Wiki Activity

chore(code): Do not alias ipython Markdown import

Browse source
This commit is contained in:
Marty Oehme 2024-07-15 20:22:23 +02:00
parent 740350eacd
commit 562b1eb6a0
Signed by: Marty
GPG key ID: EDBF2ED917B2EF6A
3 changed files with 13 additions and 13 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

2
01-codechunks/_prep-data.py
View file

@ -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

10
article.qmd
View file

@ -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

14
scoping_review.qmd
View file

@ -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.