chore(code): Rename prisma calculation variables
Renamed intermediate calculation vars from long and redundant names to slightly shorter and more coherent versions.
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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
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```{python}
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from src.model import prisma
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nr = prisma.PrismaNumbers()
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p = prisma.PrismaNumbers()
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```
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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`.
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The query execution results in an initial sample of
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`{python} p.raw_db`
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potential studies identified from the database search as well as
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`{python} p.raw_snowball`
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potential studies from other sources,
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leading to a total initial number of
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`{python} p.raw_full`.
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This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.
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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,
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Of these,
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`{python} p.dedup_full - p.out_title - p.out_abstract - p.out_language`
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have been identified as potentially relevant studies for the purposes of this scoping review and selected for a full text review,
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from which in turn
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`{python} p.final_extracted`
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have ultimately been extracted.
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@fig-intervention-types shows the predominant interventions contained in the reviewed literature.
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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
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```{python}
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from src.model import prisma
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nr = prisma.PrismaNumbers()
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p = prisma.PrismaNumbers()
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```
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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`.
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The query execution results in an initial sample of
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`{python} p.raw_db`
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potential studies identified from the database search as well as
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`{python} p.raw_snowball`
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potential studies from other sources,
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leading to a total initial number of
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`{python} p.raw_full`.
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This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.
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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,
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from which in turn `{python} nr.nr_extraction_done` have ultimately been extracted.
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Of these,
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`{python} p.dedup_full - p.out_title - p.out_abstract - p.out_language`
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have been identified as potentially relevant studies for the purposes of this scoping review and selected for a full text review,
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from which in turn
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`{python} p.final_extracted`
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have ultimately been extracted.
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The currently identified literature rises somewhat in volume over time,
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with first larger outputs identified from 2014,
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@ -1,48 +1,67 @@
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from src.process.generate_dataframes import bib_sample_raw_db, bib_sample
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from src.process.generate_dataframes import bib_sample, bib_sample_raw_db
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class PrismaNumbers:
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nr_database_query_raw = len(bib_sample_raw_db.entries)
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nr_snowballing_raw = 2240
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raw_db = len(bib_sample_raw_db.entries)
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raw_snowball = 2240
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all_keywords = [entry["keywords"] for entry in bib_sample.entries if "keywords" in entry.fields_dict.keys()]
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nr_database_deduplicated = len([1 for kw in all_keywords if "sample::database" in kw])
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nr_snowballing_deduplicated = len([1 for kw in all_keywords if "sample::snowballing" in kw])
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nr_out_superseded = len([1 for kw in all_keywords if "out::superseded" in kw])
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# list of all keywords (semicolon-delimited string) for each entry in sample
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all_kw = [
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entry["keywords"]
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for entry in bib_sample.entries
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if "keywords" in entry.fields_dict.keys()
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]
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FULL_RAW_SAMPLE_NOTHING_REMOVED = nr_database_query_raw + nr_snowballing_raw
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FULL_SAMPLE_DUPLICATES_REMOVED = nr_database_deduplicated + nr_snowballing_deduplicated + nr_out_superseded
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# calculate deduplicated and superseded amounts
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dedup_db = len([1 for kw in all_kw if "sample::database" in kw])
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dedup_snowball = len([1 for kw in all_kw if "sample::snowballing" in kw])
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out_superseded = len([1 for kw in all_kw if "out::superseded" in kw])
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raw_full = raw_db + raw_snowball
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dedup_full = dedup_db + dedup_snowball + out_superseded
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# additional non-captured numbers
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NON_ZOTERO_CAPTURE_TITLE_REMOVAL = 1150
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NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL = 727
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NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL = 348
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nr_out_duplicates = FULL_RAW_SAMPLE_NOTHING_REMOVED - FULL_SAMPLE_DUPLICATES_REMOVED
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nr_out_title = len([1 for kw in all_keywords if "out::title" in kw]) + NON_ZOTERO_CAPTURE_TITLE_REMOVAL
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nr_out_abstract = len([1 for kw in all_keywords if "out::abstract" in kw]) + NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL
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nr_out_fulltext = len([1 for kw in all_keywords if "out::full-text" in kw]) + NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL
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nr_out_language = len([1 for kw in all_keywords if "out::language" in kw])
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nr_extraction_done = len([1 for kw in all_keywords if "done::extracted" in kw])
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out_duplicates = raw_full - dedup_full
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out_title = (
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len([1 for kw in all_kw if "out::title" in kw])
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+ NON_ZOTERO_CAPTURE_TITLE_REMOVAL
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)
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out_abstract = (
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len([1 for kw in all_kw if "out::abstract" in kw])
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+ NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL
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)
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out_fulltext = (
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len([1 for kw in all_kw if "out::full-text" in kw])
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+ NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL
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)
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out_language = len([1 for kw in all_kw if "out::language" in kw])
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final_extracted = len([1 for kw in all_kw if "done::extracted" in kw])
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del bib_sample, bib_sample_raw_db
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if __name__ == "__main__":
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nr = PrismaNumbers()
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prisma = PrismaNumbers()
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# FIXME use data/supplementary undeduplciated counts to get database starting and snowballing counts
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outp = f"""
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flowchart TD;
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search_db["Records identified through database searching (n={nr.nr_database_query_raw})"] --> starting_sample;
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search_prev["Records identified through other sources (n={nr.nr_snowballing_raw})"] --> starting_sample["Starting sample (n={nr.FULL_RAW_SAMPLE_NOTHING_REMOVED})"];
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search_db["Records identified through database searching (n={prisma.raw_db})"] --> starting_sample;
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search_prev["Records identified through other sources (n={prisma.raw_snowball})"] --> starting_sample["Starting sample (n={prisma.raw_full})"];
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starting_sample -- "Duplicate removal ({nr.nr_out_duplicates+nr.nr_out_superseded} removed) "--> dedup["Records after duplicates removed (n={nr.FULL_SAMPLE_DUPLICATES_REMOVED})"];
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starting_sample -- "Duplicate removal ({prisma.out_duplicates+prisma.out_superseded} removed) "--> dedup["Records after duplicates removed (n={prisma.dedup_full})"];
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dedup -- "Title screening ({nr.nr_out_title} excluded)" --> title_screened["Records after titles screened (n={nr.FULL_SAMPLE_DUPLICATES_REMOVED - nr.nr_out_title})"];
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dedup -- "Title screening ({prisma.out_title} excluded)" --> title_screened["Records after titles screened (n={prisma.dedup_full - prisma.out_title})"];
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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})"];
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title_screened -- "Abstract screening ({prisma.out_abstract} excluded)"--> abstract_screened["Records after abstracts screened (n={prisma.dedup_full-prisma.out_title-prisma.out_abstract})"];
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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})"];
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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})"];
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language_screened -- " Full-text screening ({nr.nr_out_fulltext} excluded) "--> full-text_screened["Full-text articles assessed for eligibility (n={nr.nr_extraction_done})"];
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language_screened -- " Full-text screening ({prisma.out_fulltext} excluded) "--> full-text_screened["Full-text articles assessed for eligibility (n={prisma.final_extracted})"];
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"""
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print(outp)
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