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Recommendations for the extraction, analysis, and presentation of results in scoping reviews

2022· article· en· 1,717 citations· W4295082022 on OpenAlex· 10.11124/jbies-22-00123

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Opus teacher head0.612
GPT teacher head0.558
Teacher spread
0.054 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

Scoping reviewers often face challenges in the extraction, analysis, and presentation of scoping review results. Using best-practice examples and drawing on the expertise of the JBI Scoping Review Methodology Group and an editor of a journal that publishes scoping reviews, this paper expands on existing JBI scoping review guidance. The aim of this article is to clarify the process of extracting data from different sources of evidence; discuss what data should be extracted (and what should not); outline how to analyze extracted data, including an explanation of basic qualitative content analysis; and offer suggestions for the presentation of results in scoping reviews.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

The record

Venue
JBI Evidence Synthesis
Topic
Meta-analysis and systematic reviews
Field
Decision Sciences
Canadian institutions
Queen's UniversityPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
Funders
Keywords
Presentation (obstetrics)Data extractionBest practiceComputer scienceData scienceSystematic reviewProcess (computing)Management scienceMEDLINEEngineeringMedicinePolitical science
Has abstract in OpenAlex
yes