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Record W2990012798

Review Typology: The Basic Types of Reviews for Synthesizing Evidence for the Purpose of Knowledge Translation.

2017· review· en· W2990012798 on OpenAlexaff
Sunil Samnani, Marcus Vaska, Salim Ahmed, Tanvir Chowdhury Turin

Bibliographic record

VenuePubMed · 2017
Typereview
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsTypologyTerminologyComputer scienceStrengths and weaknessesConfusionData scienceKnowledge translationMedical literatureSystematic reviewManagement scienceKnowledge managementPsychologyMEDLINEMedicineLinguisticsEngineeringSociology
DOInot available

Abstract

fetched live from OpenAlex

With advances in medical practice and fields of research, reviews occupy a key position for summarizing existing knowledge. Due to the differences and overlap in terminology, the full potential for reviews may be lost due to confusion of indistinct approaches. The main objective of this study was to provide a descriptive outline of each of the common review types with their characteristics and examples in a health care system. Ascoping search was conducted using the keywords associated with the literature review typology. The SALSA(Search, Appraisal, Synthesis and Analysis) analytical framework was used to identify and distinguish each type of review. Nine common types of reviews and associated methodologies were evaluated against the already established SALSA framework. Their description, strengths and weaknesses are presented. The results provided a basic idea of different types of reviews based on the intended level of knowledge synthesis by which researchers can identify the appropriate type of review based on their intended audience.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.036
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.876
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

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.

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

Opus teacher head0.908
GPT teacher head0.660
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations63
Published2017
Admission routes1
Has abstractyes

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