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Record W2412306466 · doi:10.7748/nm.21.5.30.e1242

Mapping the landscape of knowledge synthesis

2014· article· en· W2412306466 on OpenAlex

Why this work is in the frame

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

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNursing Management · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRigourKnowledge translationComputer scienceKnowledge managementProcess (computing)Health careData scienceTranslation (biology)Management scienceEngineeringMathematicsPolitical science

Abstract

fetched live from OpenAlex

Knowledge translation is the means by which evidence-based practice is used in health care. Knowledge synthesis, a foundational element of knowledge translation, is a systematic, transparent, reproducible, efficient and scientific approach to identifying and summarising research findings for generalisable and consistent messages. Increasing numbers of knowledge synthesis methods are being applied to various types of research and, although these methods take similar approaches, they vary in rigour, process and resources. This article maps knowledge synthesis methods, by describing the specific stages, approaches and processes, and describes and compares different types of knowledge synthesis to help inform healthcare practitioners and policy makers about various designs. It also recommends a map of knowledge-synthesis designs for international agreement.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.153
GPT teacher head0.461
Teacher spread0.307 · 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