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feat(code): Add prisma calculation

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Marty Oehme 2024-07-15 20:54:58 +02:00
parent 0d05ed981a
commit 38254d1605
Signed by: Marty
GPG key ID: EDBF2ED917B2EF6A
7 changed files with 99 additions and 62 deletions
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45
01-codechunks/_prisma-flowchart.py
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@ -1,45 +0,0 @@
nr_database_query_raw = len(bib_sample_raw_db.entries)
nr_snowballing_raw = 2240
all_keywords = [entry["keywords"] for entry in bib_sample.entries if "keywords" in entry.fields_dict.keys()]
nr_database_deduplicated = len([1 for kw in all_keywords if "sample::database" in kw])
nr_snowballing_deduplicated = len([1 for kw in all_keywords if "sample::snowballing" in kw])
nr_out_superseded = len([1 for kw in all_keywords if "out::superseded" in kw])
FULL_RAW_SAMPLE_NOTHING_REMOVED = nr_database_query_raw + nr_snowballing_raw
FULL_SAMPLE_DUPLICATES_REMOVED = nr_database_deduplicated + nr_snowballing_deduplicated + nr_out_superseded
NON_ZOTERO_CAPTURE_TITLE_REMOVAL = 1150
NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL = 727
NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL = 348
nr_out_duplicates = FULL_RAW_SAMPLE_NOTHING_REMOVED - FULL_SAMPLE_DUPLICATES_REMOVED
nr_out_title = len([1 for kw in all_keywords if "out::title" in kw]) + NON_ZOTERO_CAPTURE_TITLE_REMOVAL
nr_out_abstract = len([1 for kw in all_keywords if "out::abstract" in kw]) + NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL
nr_out_fulltext = len([1 for kw in all_keywords if "out::full-text" in kw]) + NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL
nr_out_language = len([1 for kw in all_keywords if "out::language" in kw])
nr_extraction_done = len([1 for kw in all_keywords if "done::extracted" in kw])
t3 = "`" * 3
# FIXME use 02-data/supplementary undeduplciated counts to get database starting and snowballing counts
# from: https://github.com/quarto-dev/quarto-cli/discussions/6508
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={nr_database_query_raw})"] --> starting_sample;
search_prev["Records identified through other sources (n={nr_snowballing_raw})"] --> starting_sample["Starting sample (n={FULL_RAW_SAMPLE_NOTHING_REMOVED})"];
starting_sample -- "Duplicate removal ({nr_out_duplicates+nr_out_superseded} removed) "--> dedup["Records after duplicates removed (n={FULL_SAMPLE_DUPLICATES_REMOVED})"];
dedup -- "Title screening ({nr_out_title} excluded)" --> title_screened["Records after titles screened (n={FULL_SAMPLE_DUPLICATES_REMOVED - nr_out_title})"];
title_screened -- "Abstract screening ({nr_out_abstract} excluded)"--> abstract_screened["Records after abstracts screened (n={FULL_SAMPLE_DUPLICATES_REMOVED-nr_out_title-nr_out_abstract})"];
abstract_screened -- " Language screening ({nr_out_language} excluded) "--> language_screened["Records after language screened (n={FULL_SAMPLE_DUPLICATES_REMOVED-nr_out_title-nr_out_abstract-nr_out_language})"];
language_screened -- " Full-text screening ({nr_out_fulltext} excluded) "--> full-text_screened["Full-text articles assessed for eligibility (n={nr_extraction_done})"];
{t3}
""")

15
02-data/processed/prisma.mmd Normal file
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@ -0,0 +1,15 @@
flowchart TD;
search_db["Records identified through database searching (n=1749)"] --> starting_sample;
search_prev["Records identified through other sources (n=2240)"] --> starting_sample["Starting sample (n=3989)"];
starting_sample -- "Duplicate removal (267 removed) "--> dedup["Records after duplicates removed (n=3723)"];
dedup -- "Title screening (1779 excluded)" --> title_screened["Records after titles screened (n=1944)"];
title_screened -- "Abstract screening (1506 excluded)"--> abstract_screened["Records after abstracts screened (n=438)"];
abstract_screened -- " Language screening (2 excluded) "--> language_screened["Records after language screened (n=436)"];
language_screened -- " Full-text screening (383 excluded) "--> full-text_screened["Full-text articles assessed for eligibility (n=52)"];

3
Makefile
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@ -8,6 +8,9 @@ render: scoping_review.qmd
extract: scoping_review.qmd
poetry extract
poetry prisma
release: scoping_review.qmd
poetry extract
poetry prisma
poetry milestone

19
article.qmd
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@ -136,20 +136,23 @@ they will in turn be crawled for cited sources in a 'snowballing' process.
The sources will be added to the sample to undergo the same screening process explained above,
ultimately resulting in the process represented in the PRISMA chart in @fig-prisma.
```{python}
#| label: calculate-scoping-flowchart
#| echo: false
#| output: asis
{{< include 01-codechunks/_prisma-flowchart.py >}}
```{mermaid}
%%| label: fig-prisma
%%| fig-cap: PRISMA flowchart for scoping process
%%| file: 02-data/processed/prisma.mmd
```
All relevant data concerning both their major findings and statistical significance are then extracted from the individual studies into a collective results matrix.
The results to be identified in the matrix include a study's: i) key outcome measures (dependent variables), ii) main findings, iii) main policy interventions (independent variables), iv) study design and sample size, v) dataset and methods of evaluation, vi) direction of relation and level of representativeness, vii) level of statistical significance, viii) main limitations.
The query execution results in an initial sample of `{python} nr_database_query_raw` potential studies identified from the database search as well as `{python} nr_snowballing_raw` potential studies from other sources, leading to a total initial number of `{python} FULL_RAW_SAMPLE_NOTHING_REMOVED`.
```{python}
from src.model import prisma
nr = prisma.PrismaNumbers()
```
The query execution results in an initial sample of `{python} nr.nr_database_query_raw` potential studies identified from the database search as well as `{python} nr.nr_snowballing_raw` potential studies from other sources, leading to a total initial number of `{python} nr.FULL_RAW_SAMPLE_NOTHING_REMOVED`.
This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.
Of these, `{python} FULL_SAMPLE_DUPLICATES_REMOVED-nr_out_title-nr_out_abstract-nr_out_language` have been identified as potentially relevant studies for the purposes of this scoping review and selected for a full text review,
from which in turn `{python} nr_extraction_done` have ultimately been extracted.
Of these, `{python} nr.FULL_SAMPLE_DUPLICATES_REMOVED-nr.nr_out_title-nr.nr_out_abstract-nr.nr_out_language` have been identified as potentially relevant studies for the purposes of this scoping review and selected for a full text review,
@fig-intervention-types shows the predominant interventions contained in the reviewed literature.
Overall, there is a focus on measures of minimum wage, subsidisation, considerations of trade liberalisation and collective bargaining, education and training.

6
pyproject.toml
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@ -45,11 +45,15 @@ help = "Extract the csv data from raw yaml files"
shell = """
python src/extract/raw_to_extracted_csv.py > 02-data/processed/extracted.csv
"""
[tool.poe.tasks.prisma]
help = "Update PRISMA flowchart numbers"
shell = """
python src/model/prisma.py > 02-data/processed/prisma.mmd
"""
[tool.poe.tasks.milestone]
help = "Extract, render, commit and version a finished artifact"
shell = """
quarto render --output-dir 05-final_paper
poe extract
VERSION="$(poetry version -s minor)"
git add pyproject.toml 02-data 05-final_paper
git commit -m "Publish version $VERSION" --no-gpg-sign

21
scoping_review.qmd
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@ -363,12 +363,12 @@ Last, for extraction, studies are screened for their full-texts, irrelevant stud
Should any literature reviews be identified as relevant during this screening process,
they will in turn be crawled for cited sources in a 'snowballing' process,
and the sources will be added to the sample to undergo the same screening process explained above.
The resulting process can be seen in @fig-prisma.
```{python}
#| label: calculate-scoping-flowchart
#| echo: false
#| output: asis
{{< include 01-codechunks/_prisma-flowchart.py >}}
```{mermaid}
%%| label: fig-prisma
%%| fig-cap: PRISMA flowchart for scoping process
%%| file: 02-data/processed/prisma.mmd
```
All relevant data concerning both their major findings and statistical significance are then extracted from the individual studies into a collective results matrix.
@ -386,10 +386,15 @@ For a full list of validity ranks, see @apptbl-validity-external and @apptbl-val
## Data
The query execution results in an initial sample of `{python} nr_database_query_raw` potential studies identified from the database search as well as `{python} nr_snowballing_raw` potential studies from other sources, leading to a total initial number of `{python} FULL_RAW_SAMPLE_NOTHING_REMOVED`.
```{python}
from src.model import prisma
nr = prisma.PrismaNumbers()
```
The query execution results in an initial sample of `{python} nr.nr_database_query_raw` potential studies identified from the database search as well as `{python} nr.nr_snowballing_raw` potential studies from other sources, leading to a total initial number of `{python} nr.FULL_RAW_SAMPLE_NOTHING_REMOVED`.
This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.
Of these, `{python} FULL_SAMPLE_DUPLICATES_REMOVED-nr_out_title-nr_out_abstract-nr_out_language` have been identified as potentially relevant studies for the purposes of this scoping review and selected for a full text review,
from which in turn `{python} nr_extraction_done` have ultimately been extracted.
Of these, `{python} nr.FULL_SAMPLE_DUPLICATES_REMOVED-nr.nr_out_title-nr.nr_out_abstract-nr.nr_out_language` have been identified as potentially relevant studies for the purposes of this scoping review and selected for a full text review,
from which in turn `{python} nr.nr_extraction_done` have ultimately been extracted.
The currently identified literature rises somewhat in volume over time,
with first larger outputs identified from 2014,

52
src/model/prisma.py Normal file
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@ -0,0 +1,52 @@
from src.process import add_metadata as meta
from src import globals as g
bib_sample_raw_db = meta.bib_library_from_dir(g.RAW_DATA)
bib_sample = meta.bib_library_from_dir(g.WORKING_DATA)
class PrismaNumbers:
nr_database_query_raw = len(bib_sample_raw_db.entries)
nr_snowballing_raw = 2240
all_keywords = [entry["keywords"] for entry in bib_sample.entries if "keywords" in entry.fields_dict.keys()]
nr_database_deduplicated = len([1 for kw in all_keywords if "sample::database" in kw])
nr_snowballing_deduplicated = len([1 for kw in all_keywords if "sample::snowballing" in kw])
nr_out_superseded = len([1 for kw in all_keywords if "out::superseded" in kw])
FULL_RAW_SAMPLE_NOTHING_REMOVED = nr_database_query_raw + nr_snowballing_raw
FULL_SAMPLE_DUPLICATES_REMOVED = nr_database_deduplicated + nr_snowballing_deduplicated + nr_out_superseded
NON_ZOTERO_CAPTURE_TITLE_REMOVAL = 1150
NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL = 727
NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL = 348
nr_out_duplicates = FULL_RAW_SAMPLE_NOTHING_REMOVED - FULL_SAMPLE_DUPLICATES_REMOVED
nr_out_title = len([1 for kw in all_keywords if "out::title" in kw]) + NON_ZOTERO_CAPTURE_TITLE_REMOVAL
nr_out_abstract = len([1 for kw in all_keywords if "out::abstract" in kw]) + NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL
nr_out_fulltext = len([1 for kw in all_keywords if "out::full-text" in kw]) + NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL
nr_out_language = len([1 for kw in all_keywords if "out::language" in kw])
nr_extraction_done = len([1 for kw in all_keywords if "done::extracted" in kw])
del bib_sample, bib_sample_raw_db
if __name__ == "__main__":
nr = PrismaNumbers()
# FIXME use 02-data/supplementary undeduplciated counts to get database starting and snowballing counts
outp = f"""
flowchart TD;
search_db["Records identified through database searching (n={nr.nr_database_query_raw})"] --> starting_sample;
search_prev["Records identified through other sources (n={nr.nr_snowballing_raw})"] --> starting_sample["Starting sample (n={nr.FULL_RAW_SAMPLE_NOTHING_REMOVED})"];
starting_sample -- "Duplicate removal ({nr.nr_out_duplicates+nr.nr_out_superseded} removed) "--> dedup["Records after duplicates removed (n={nr.FULL_SAMPLE_DUPLICATES_REMOVED})"];
dedup -- "Title screening ({nr.nr_out_title} excluded)" --> title_screened["Records after titles screened (n={nr.FULL_SAMPLE_DUPLICATES_REMOVED - nr.nr_out_title})"];
title_screened -- "Abstract screening ({nr.nr_out_abstract} excluded)"--> abstract_screened["Records after abstracts screened (n={nr.FULL_SAMPLE_DUPLICATES_REMOVED-nr.nr_out_title-nr.nr_out_abstract})"];
abstract_screened -- " Language screening ({nr.nr_out_language} excluded) "--> language_screened["Records after language screened (n={nr.FULL_SAMPLE_DUPLICATES_REMOVED-nr.nr_out_title-nr.nr_out_abstract-nr.nr_out_language})"];
language_screened -- " Full-text screening ({nr.nr_out_fulltext} excluded) "--> full-text_screened["Full-text articles assessed for eligibility (n={nr.nr_extraction_done})"];
"""
print(outp)