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feat(script): Use new in-text computation interpolations

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Can now access python variables directly from markdown text in-flow.
We can make use of this to dynamically update the numbers for scoping
review steps in the descriptive part.
This commit is contained in:
Marty Oehme 2024-02-14 15:53:42 +01:00
parent 2de93d506f
commit 6554b0f8e9
Signed by: Marty
GPG key ID: EDBF2ED917B2EF6A
1 changed files with 6 additions and 6 deletions
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scoping_review.qmd
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@ -111,6 +111,8 @@ df_country_groups = None
<!-- pagebreak to separate from TOC -->
{{< pagebreak >}}
{{< portrait >}}
# Introduction
This study presents a systematic scoping review of the current literature concerning inequalities in the world of work.
@ -481,14 +483,12 @@ The results to be identified in the matrix include a study’s: i) key outcome m
#| echo: false
# TODO Remove redundant 'relevant' studies observation below once all studies are extracted.
nr_relevant = len([1 for kw in all_keywords if "relevant" in kw])
md(f"""
The query execution results in an initial sample of {nr_database_query_raw} potential studies identified from the database search as well as {nr_other_sources} potential studies from other sources, leading to a total initial number of {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This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.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, {nr_relevant} have been identified as potentially relevant studies for the purposes of this scoping review, from which {nr_extraction_done} have been extracted.
""")
```
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_other_sources` potential studies from other sources, leading to a total initial number of `{python} 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This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.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} nr_relevant` have been identified as potentially relevant studies for the purposes of this scoping review, from which `{python} nr_extraction_done` have been extracted.
<!-- {{++ FIXME: Update description for changing study pool ++}} -->
The currently identified literature rises somewhat in volume over time,