effort_initialize: Formats a reference dataset for title/abstract screening efforts.
Description
Adds columns with standardized labels to a data framw with bibliographic data
on journal articles. These columns will be used to assign reviewers,
implementation of dual screening design, and the coding of
inclusion/exclusions screening decisions.
Usage
effort_initialize(
aDataFrame,
study_ID = TRUE,
unscreenedValue = "not vetted",
dual = FALSE,
front = TRUE
)
Arguments
aDataFrame
A data.frame object that includes the titles and
abstracts to be screened. It will be formatted for screening efforts.
See example: example_references_metagear
study_ID
When FALSE
, does not add a column "STUDY_ID" that
includes a unique identification number for each reference (row) in the
dataFrame.
unscreenedValue
Changes the default coding (a string) of "not vetted"
that designates whether an abstract remains to be screened or vetted as
part of the "INCLUDE" column.
dual
When TRUE
, formats dataFrame for a dual screening (paired)
design. Creates two reviewer teams: REVIEWERS_A and REVIEWERS_B.
front
When FALSE
, adds new columns to the back end of the
dataframe. When TRUE
, adds columns to the front.
Value
A data.frame formatted for title/abstract screening efforts.
Examples
Run this code# NOT RUN {
data(example_references_metagear)
effort_initialize(example_references_metagear)
# }
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