chore(code): Rename prisma calculation variables

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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@ -160,12 +160,23 @@ The results to be identified in the matrix include a study's: i) key outcome mea
```{python}
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This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.
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.
Overall, there is a focus on measures of minimum wage, subsidisation, considerations of trade liberalisation and collective bargaining, education and training.

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@ -403,13 +403,23 @@ For a full list of validity ranks, see @apptbl-validity-external and @apptbl-val
```{python}
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.
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.
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.
The currently identified literature rises somewhat in volume over time,
with first larger outputs identified from 2014,

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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:
nr_database_query_raw = len(bib_sample_raw_db.entries)
nr_snowballing_raw = 2240
raw_db = len(bib_sample_raw_db.entries)
raw_snowball = 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])
# list of all keywords (semicolon-delimited string) for each entry in sample
all_kw = [
entry["keywords"]
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
FULL_SAMPLE_DUPLICATES_REMOVED = nr_database_deduplicated + nr_snowballing_deduplicated + nr_out_superseded
# calculate deduplicated and superseded amounts
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_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])
out_duplicates = raw_full - dedup_full
out_title = (
len([1 for kw in all_kw if "out::title" in kw])
+ NON_ZOTERO_CAPTURE_TITLE_REMOVAL
)
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
if __name__ == "__main__":
nr = PrismaNumbers()
prisma = PrismaNumbers()
# FIXME use 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})"];
search_db["Records identified through database searching (n={prisma.raw_db})"] --> starting_sample;
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)