chore(repo): Move manuscripts to separate dir
Both the manuscripts for the working paper and the article are now collected in a separate manuscripts folder.
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7 changed files with 31 additions and 39 deletions
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@ -1,7 +1,7 @@
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project:
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type: default
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render:
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- article.qmd
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- manuscript/article.qmd
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format:
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elsevier-html:
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@ -1,9 +1,9 @@
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project:
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render:
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- presentation_summary.md
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- notes.qmd
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- meeting_eoy.qmd
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- scoping_review.qmd
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- manuscript/presentation_summary.md
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- manuscript/notes.qmd
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- manuscript/meeting_eoy.qmd
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- manuscript/scoping_review.qmd
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toc: true
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format:
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@ -102,7 +102,7 @@ with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
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::: {#tbl-inclusion-criteria}
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```{python}
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inclusion_criteria = pd.read_csv("data/supplementary/inclusion-criteria.tsv", sep="\t")
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inclusion_criteria = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/inclusion-criteria.tsv", sep="\t")
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Markdown(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
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```
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@ -152,7 +152,7 @@ ultimately resulting in the process represented in the PRISMA chart in @fig-pris
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```{mermaid}
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%%| label: fig-prisma
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%%| fig-cap: PRISMA flowchart for scoping process
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%%| file: data/processed/prisma.mmd
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%%| file: ../data/processed/prisma.mmd
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```
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All relevant data concerning both their major findings and statistical significance are then extracted from the individual studies into a collective results matrix.
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@ -225,7 +225,7 @@ def strength_for(val):
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]
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findings_institutional = pd.read_csv("data/supplementary/findings-institutional.csv")
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findings_institutional = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/findings-institutional.csv")
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outp = Markdown(
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tabulate(
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@ -695,7 +695,7 @@ Another reason could be the actual implementation of different policy programmes
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::: {#appatbl-wow-terms}
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```{python}
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terms_wow = pd.read_csv("data/supplementary/terms_wow.csv")
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terms_wow = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_wow.csv")
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Markdown(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
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```
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@ -706,7 +706,7 @@ World of work term cluster
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::: {#appatbl-intervention-terms}
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```{python}
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terms_policy = pd.read_csv("data/supplementary/terms_policy.csv")
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terms_policy = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_policy.csv")
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# different headers to include 'social norms'
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headers = ["General", "Institutional", "Structural", "Agency & social norms"]
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Markdown(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
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@ -719,7 +719,7 @@ Policy intervention term cluster
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::: {#appatbl-inequality-terms}
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```{python}
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terms_inequality = pd.read_csv("data/supplementary/terms_inequality.csv")
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terms_inequality = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_inequality.csv")
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Markdown(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
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```
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@ -1,5 +1,5 @@
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---
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bibliography: data/intermediate/zotero-library.bib
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bibliography: ../data/intermediate/zotero-library.bib
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csl: /home/marty/documents/library/utilities/styles/APA-7.csl
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papersize: A4
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linestretch: 1.5
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@ -30,26 +30,22 @@ import bibtexparser
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sns.set_style("whitegrid")
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DATA_DIR=Path("./data")
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RAW_DATA=DATA_DIR.joinpath("raw")
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WORKING_DATA=DATA_DIR.joinpath("intermediate")
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PROCESSED_DATA=DATA_DIR.joinpath("processed")
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SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")
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from src import globals as g
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bib_string=""
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for partial_bib in RAW_DATA.glob("**/*.bib"):
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for partial_bib in g.RAW_DATA.glob("**/*.bib"):
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with open(partial_bib) as f:
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bib_string+="\n".join(f.readlines())
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bib_sample_raw_db = bibtexparser.parse_string(bib_string)
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bib_string=""
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for partial_bib in WORKING_DATA.glob("**/*.bib"):
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for partial_bib in g.WORKING_DATA.glob("**/*.bib"):
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with open(partial_bib) as f:
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bib_string+="\n".join(f.readlines())
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bib_sample = bibtexparser.parse_string(bib_string)
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# load relevant studies
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from src import load_data
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from src.extract import load_data
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# load zotero-based metadata: citations and uses
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zot_df = pd.DataFrame([
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@ -63,10 +59,10 @@ zot_df = pd.DataFrame([
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], columns = ["doi", "cited", "usage", "keywords"]).drop_duplicates("doi").set_index("doi")
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# Add WB country grouping definitions (income group, world region)
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WB_COUNTRY_GROUPS_FILE = Path(f"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx").resolve()
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WB_COUNTRY_GROUPS_FILE = Path(f"{g.SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx").resolve()
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df_country_groups = pd.read_excel(WB_COUNTRY_GROUPS_FILE).set_index("Economy")
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bib_df = (load_data.from_yml(f"{PROCESSED_DATA}/relevant")
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bib_df = (load_data.from_yml(f"{g.PROCESSED_DATA}/relevant")
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.assign(
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doi=lambda _df: _df["uri"].str.extract(r"https?://(?:dx\.)?doi\.org/(.*)", expand=False),
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zot_cited=lambda _df: _df["doi"].map(zot_df["cited"]),
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@ -1,5 +1,5 @@
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---
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bibliography: data/intermediate/zotero-library.bib
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bibliography: ../data/intermediate/zotero-library.bib
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csl: /home/marty/documents/library/utilities/styles/APA-7.csl
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papersize: A4
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linestretch: 1.5
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@ -21,12 +21,7 @@ subtitle: Conceptual Definitions and Key Terms
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```{python}
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#| echo: false
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from pathlib import Path
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DATA_DIR=Path("./data")
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RAW_DATA=DATA_DIR.joinpath("raw")
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WORKING_DATA=DATA_DIR.joinpath("intermediate")
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PROCESSED_DATA=DATA_DIR.joinpath("processed")
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SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")
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from src import globals as g
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## standard imports
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from IPython.core.display import Markdown as md
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@ -396,7 +391,7 @@ Policy *areas*, identified by @ILO2022b:
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#| label: tbl-inclusion-criteria
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#| tbl-cap: Study inclusion and exclusion scoping criteria {#tbl-inclusion-criteria}
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inclusion_criteria = pd.read_csv("data/supplementary/inclusion-criteria.tsv", sep="\t")
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inclusion_criteria = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/inclusion-criteria.tsv", sep="\t")
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md(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
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```
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@ -861,7 +856,7 @@ from @Pinto2021:
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```{python}
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#| echo: false
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#| output: asis
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with open(f"{SUPPLEMENTARY_DATA}/query.txt") as f:
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with open(f"{g.SUPPLEMENTARY_DATA}/query.txt") as f:
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query = f.read()
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t3 = "`" * 3
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@ -26,6 +26,7 @@ crossref:
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#| echo: false
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#| output: false
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import src.globals as g
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import re
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from IPython.display import display, Markdown, HTML
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import numpy as np
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import pandas as pd
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@ -303,7 +304,7 @@ with the search query requiring a term from the general column and one other col
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```{python}
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#| label: tbl-wow-terms
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#| tbl-cap: World of work term cluster
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terms_wow = pd.read_csv("data/supplementary/terms_wow.csv")
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terms_wow = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_wow.csv")
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Markdown(tabulate(terms_wow.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
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```
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@ -319,7 +320,7 @@ For the database query, a single term from the general category is required to b
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```{python}
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#| label: tbl-intervention-terms
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#| tbl-cap: Policy intervention term cluster
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terms_policy = pd.read_csv("data/supplementary/terms_policy.csv")
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terms_policy = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_policy.csv")
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# different headers to include 'social norms'
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headers = ["General", "Institutional", "Structural", "Agency & social norms"]
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Markdown(tabulate(terms_policy.fillna(""), showindex=False, headers=headers, tablefmt="grid"))
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@ -331,7 +332,7 @@ as seen in @tbl-inequality-terms.
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```{python}
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#| label: tbl-inequality-terms
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#| tbl-cap: Inequality term cluster
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terms_inequality = pd.read_csv("data/supplementary/terms_inequality.csv")
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terms_inequality = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/terms_inequality.csv")
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Markdown(tabulate(terms_inequality.fillna(""), showindex=False, headers="keys", tablefmt="grid"))
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```
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@ -355,7 +356,7 @@ with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.
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```{python}
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#| label: inclusion-criteria
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inclusion_criteria = pd.read_csv("data/supplementary/inclusion-criteria.tsv", sep="\t")
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inclusion_criteria = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/inclusion-criteria.tsv", sep="\t")
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Markdown(tabulate(inclusion_criteria, showindex=False, headers="keys", tablefmt="grid"))
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```
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@ -382,7 +383,7 @@ The resulting process can be seen in @fig-prisma.
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```{mermaid}
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%%| label: fig-prisma
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%%| fig-cap: PRISMA flowchart for scoping process
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%%| file: data/processed/prisma.mmd
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%%| file: ../data/processed/prisma.mmd
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```
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All relevant data concerning both their major findings and statistical significance are then extracted from the individual studies into a collective results matrix.
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@ -559,7 +560,7 @@ study_strength_bins = {
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def strength_for(val):
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return list(study_strength_bins.keys())[list(study_strength_bins.values()).index(val)]
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findings_institutional = pd.read_csv("data/supplementary/findings-institutional.csv")
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findings_institutional = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/findings-institutional.csv")
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fd_df = validity.add_to_findings(findings_institutional, by_intervention, study_strength_bins)
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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"))
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#| label: tbl-findings-structural
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from src.model import validity
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findings_structural = pd.read_csv("data/supplementary/findings-structural.csv")
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findings_structural = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/findings-structural.csv")
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fd_df = validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)
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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"))
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#| label: tbl-findings-agency
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from src.model import validity
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findings_agency = pd.read_csv("data/supplementary/findings-agency.csv")
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findings_agency = pd.read_csv(f"{g.SUPPLEMENTARY_DATA}/findings-agency.csv")
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fd_df = validity.add_to_findings(findings_agency, by_intervention, study_strength_bins)
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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"))
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