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Record W2174826059 · doi:10.1186/s13012-015-0351-9

Exploring the function and effectiveness of knowledge brokers as facilitators of knowledge translation in health-related settings: a systematic review and thematic analysis

2015· review· en· W2174826059 on OpenAlexafffund
Catherine Bornbaum, Kathy Kornas, Leslea Peirson, Laura C. Rosella

Bibliographic record

VenueImplementation Science · 2015
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsInstitute for Clinical Evaluative SciencesWestern UniversityMcMaster UniversityUniversity of TorontoPublic Health Ontario
FundersCanadian Institutes of Health Research
KeywordsKnowledge translationCINAHLPsycINFOThematic analysisGrey literatureHealth informaticsKnowledge managementMedicineMEDLINEScopusContext (archaeology)Health administrationKnowledge transferQualitative researchMedical educationComputer scienceNursingPublic healthPsychological interventionSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Knowledge brokers (KBs) work collaboratively with key stakeholders to facilitate the transfer and exchange of information in a given context. Currently, there is a perceived lack of evidence about the effectiveness of knowledge brokering and the factors that influence its success as a knowledge translation (KT) mechanism. Thus, the goal of this review was to systematically gather evidence regarding the nature of knowledge brokering in health-related settings and determine if KBs effectively contributed to KT in these settings. METHODS: A systematic review was conducted using a search strategy designed by a health research librarian. Eight electronic databases (MEDLINE, Embase, PsycINFO, CINAHL, ERIC, Scopus, SocINDEX, and Health Business Elite) and relevant grey literature sources were searched using English language restrictions. Two reviewers independently screened the abstracts, reviewed full-text articles, extracted data, and performed quality assessments. Analysis included a confirmatory thematic approach. To be included, studies must have occurred in a health-related setting, reported on an actual application of knowledge brokering, and be available in English. RESULTS: In total, 7935 records were located. Following removal of duplicates, 6936 abstracts were screened and 240 full-text articles were reviewed. Ultimately, 29 articles, representing 22 unique studies, were included in the thematic analysis. Qualitative (n = 18), quantitative (n = 1), and mixed methods (n = 6) designs were represented in addition to grey literature sources (n = 4). Findings indicated that KBs performed a diverse range of tasks across multiple health-related settings; results supported the KB role as a 'knowledge manager', 'linkage agent', and 'capacity builder'. Our systematic review explored outcome data from a subset of studies (n = 8) for evidence of changes in knowledge, skills, and policies or practices related to knowledge brokering. Two studies met standards for acceptable methodological rigour; thus, findings were inconclusive regarding KB effectiveness. CONCLUSIONS: As knowledge managers, linkage agents, and capacity builders, KBs performed many and varied tasks to transfer and exchange information across health-related stakeholders, settings, and sectors. How effectively they fulfilled their role in facilitating KT processes is unclear; further rigourous research is required to answer this question and discern the potential impact of KBs on education, practice, and policy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.125
metaresearch head score (Gemma)0.231
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.875
Threshold uncertainty score0.662

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1250.231
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0100.010
Bibliometrics0.0350.030
Science and technology studies0.0030.004
Scholarly communication0.0080.012
Open science0.0030.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.722
GPT teacher head0.680
Teacher spread0.041 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
DomainMethods
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

Citations311
Published2015
Admission routes2
Has abstractyes

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