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chore(code): Rename prisma calculation variables

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Renamed intermediate calculation vars from long and redundant names to
slightly shorter and more coherent versions.
This commit is contained in:
Marty Oehme 2024-07-16 17:47:20 +02:00
parent b230228095
commit 0d723dbfdf
Signed by: Marty
GPG key ID: EDBF2ED917B2EF6A
3 changed files with 70 additions and 30 deletions
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17
manuscript/article.qmd
View file

@ -160,12 +160,23 @@ The results to be identified in the matrix include a study's: i) key outcome mea
```{python} ```{python}
from src.model import prisma from src.model import prisma
nr = prisma.PrismaNumbers() p = 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`. The query execution results in an initial sample of
`{python} p.raw_db`
potential studies identified from the database search as well as
`{python} p.raw_snowball`
potential studies from other sources,
leading to a total initial number of
`{python} p.raw_full`.
This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria. This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.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} 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, Of these,
`{python} p.dedup_full - p.out_title - p.out_abstract - p.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} p.final_extracted`
have ultimately been extracted.
@fig-intervention-types shows the predominant interventions contained in the reviewed literature. @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. Overall, there is a focus on measures of minimum wage, subsidisation, considerations of trade liberalisation and collective bargaining, education and training.

18
manuscript/scoping_review.qmd
View file

@ -403,13 +403,23 @@ For a full list of validity ranks, see @apptbl-validity-external and @apptbl-val
```{python} ```{python}
from src.model import prisma from src.model import prisma
nr = prisma.PrismaNumbers() p = 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`. The query execution results in an initial sample of
`{python} p.raw_db`
potential studies identified from the database search as well as
`{python} p.raw_snowball`
potential studies from other sources,
leading to a total initial number of
`{python} p.raw_full`.
This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria. 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} 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, Of these,
from which in turn `{python} nr.nr_extraction_done` have ultimately been extracted. `{python} p.dedup_full - p.out_title - p.out_abstract - p.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} p.final_extracted`
have ultimately been extracted.
The currently identified literature rises somewhat in volume over time, The currently identified literature rises somewhat in volume over time,
with first larger outputs identified from 2014, with first larger outputs identified from 2014,

65
src/model/prisma.py
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@ -1,48 +1,67 @@
from src.process.generate_dataframes import bib_sample_raw_db, bib_sample from src.process.generate_dataframes import bib_sample, bib_sample_raw_db
class PrismaNumbers: class PrismaNumbers:
nr_database_query_raw = len(bib_sample_raw_db.entries) raw_db = len(bib_sample_raw_db.entries)
nr_snowballing_raw = 2240 raw_snowball = 2240
all_keywords = [entry["keywords"] for entry in bib_sample.entries if "keywords" in entry.fields_dict.keys()] # list of all keywords (semicolon-delimited string) for each entry in sample
nr_database_deduplicated = len([1 for kw in all_keywords if "sample::database" in kw]) all_kw = [
nr_snowballing_deduplicated = len([1 for kw in all_keywords if "sample::snowballing" in kw]) entry["keywords"]
nr_out_superseded = len([1 for kw in all_keywords if "out::superseded" in kw]) for entry in bib_sample.entries
if "keywords" in entry.fields_dict.keys()
]
FULL_RAW_SAMPLE_NOTHING_REMOVED = nr_database_query_raw + nr_snowballing_raw # calculate deduplicated and superseded amounts
FULL_SAMPLE_DUPLICATES_REMOVED = nr_database_deduplicated + nr_snowballing_deduplicated + nr_out_superseded dedup_db = len([1 for kw in all_kw if "sample::database" in kw])
dedup_snowball = len([1 for kw in all_kw if "sample::snowballing" in kw])
out_superseded = len([1 for kw in all_kw if "out::superseded" in kw])
raw_full = raw_db + raw_snowball
dedup_full = dedup_db + dedup_snowball + out_superseded
# additional non-captured numbers
NON_ZOTERO_CAPTURE_TITLE_REMOVAL = 1150 NON_ZOTERO_CAPTURE_TITLE_REMOVAL = 1150
NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL = 727 NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL = 727
NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL = 348 NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL = 348
nr_out_duplicates = FULL_RAW_SAMPLE_NOTHING_REMOVED - FULL_SAMPLE_DUPLICATES_REMOVED out_duplicates = raw_full - dedup_full
nr_out_title = len([1 for kw in all_keywords if "out::title" in kw]) + NON_ZOTERO_CAPTURE_TITLE_REMOVAL out_title = (
nr_out_abstract = len([1 for kw in all_keywords if "out::abstract" in kw]) + NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL len([1 for kw in all_kw if "out::title" in kw])
nr_out_fulltext = len([1 for kw in all_keywords if "out::full-text" in kw]) + NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL + NON_ZOTERO_CAPTURE_TITLE_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]) out_abstract = (
len([1 for kw in all_kw if "out::abstract" in kw])
+ NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL
)
out_fulltext = (
len([1 for kw in all_kw if "out::full-text" in kw])
+ NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL
)
out_language = len([1 for kw in all_kw if "out::language" in kw])
final_extracted = len([1 for kw in all_kw if "done::extracted" in kw])
del bib_sample, bib_sample_raw_db del bib_sample, bib_sample_raw_db
if __name__ == "__main__": if __name__ == "__main__":
nr = PrismaNumbers() prisma = PrismaNumbers()
# FIXME use data/supplementary undeduplciated counts to get database starting and snowballing counts # FIXME use data/supplementary undeduplciated counts to get database starting and snowballing counts
outp = f""" outp = f"""
flowchart TD; flowchart TD;
search_db["Records identified through database searching (n={nr.nr_database_query_raw})"] --> starting_sample; search_db["Records identified through database searching (n={prisma.raw_db})"] --> 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})"]; search_prev["Records identified through other sources (n={prisma.raw_snowball})"] --> starting_sample["Starting sample (n={prisma.raw_full})"];
starting_sample -- "Duplicate removal ({nr.nr_out_duplicates+nr.nr_out_superseded} removed) "--> dedup["Records after duplicates removed (n={nr.FULL_SAMPLE_DUPLICATES_REMOVED})"]; starting_sample -- "Duplicate removal ({prisma.out_duplicates+prisma.out_superseded} removed) "--> dedup["Records after duplicates removed (n={prisma.dedup_full})"];
dedup -- "Title screening ({nr.nr_out_title} excluded)" --> title_screened["Records after titles screened (n={nr.FULL_SAMPLE_DUPLICATES_REMOVED - nr.nr_out_title})"]; dedup -- "Title screening ({prisma.out_title} excluded)" --> title_screened["Records after titles screened (n={prisma.dedup_full - prisma.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})"]; title_screened -- "Abstract screening ({prisma.out_abstract} excluded)"--> abstract_screened["Records after abstracts screened (n={prisma.dedup_full-prisma.out_title-prisma.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})"]; abstract_screened -- " Language screening ({prisma.out_language} excluded) "--> language_screened["Records after language screened (n={prisma.dedup_full-prisma.out_title-prisma.out_abstract-prisma.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})"]; language_screened -- " Full-text screening ({prisma.out_fulltext} excluded) "--> full-text_screened["Full-text articles assessed for eligibility (n={prisma.final_extracted})"];
""" """
print(outp) print(outp)