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Record W2534132861 · doi:10.1186/s13012-016-0504-5

Knowledge brokering for healthy aging: a scoping review of potential approaches

2016· review· en· W2534132861 on OpenAlexafffundabout
Dwayne Van Eerd, Kristine Newman, Ryan DeForge, Robin Urquhart, Katie N. Dainty

Bibliographic record

VenueImplementation Science · 2016
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsSt. Michael's HospitalUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of TorontoDalhousie UniversityToronto Metropolitan UniversityUniversity of WindsorInstitute for Work & HealthUniversity of Waterloo
FundersRyerson University
KeywordsMedicineHealth informaticsHealth services researchHealth administrationPublic healthGerontologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Developing a healthcare delivery system that is more responsive to the future challenges of an aging population is a priority in Canada. The World Health Organization acknowledges the need for knowledge translation frameworks in aging and health. Knowledge brokering (KB) is a specific knowledge translation approach that includes making connections between people to facilitate the use of evidence. Knowledge gaps exist about KB roles, approaches, and guiding frameworks. The objective of the scoping review is to identify and describe KB approaches and the underlying conceptual frameworks (models, theories) used to guide the approaches that could support healthy aging. METHODS: Literature searches were done in PubMed, EMBASE, PsycINFO, EBM reviews (Cochrane Database of systematic reviews), CINAHL, and SCOPUS, as well as Google and Google Scholar using terms related to knowledge brokering. Titles, abstracts, and full reports were reviewed independently by two reviewers who came to consensus on all screening criteria. Documents were included if they described a KB approach and details about the underlying conceptual basis. Data about KB approach, target stakeholders, KB outcomes, and context were extracted independently by two reviewers. RESULTS: Searches identified 248 unique references. Screening for inclusion revealed 19 documents that described 15 accounts of knowledge brokering and details about conceptual guidance and could be applied in healthy aging contexts. Eight KB elements were detected in the approaches though not all approaches incorporated all elements. The underlying conceptual guidance for KB approaches varied. Specific KB frameworks were referenced or developed for nine KB approaches while the remaining six cited more general KT frameworks (or multiple frameworks) as guidance. CONCLUSIONS: The KB approaches that we found varied greatly depending on the context and stakeholders involved. Three of the approaches were explicitly employed in the context of health aging. Common elements of KB approaches that could be conducted in healthy aging contexts focussed on acquiring, adapting, and disseminating knowledge and networking (linkage). The descriptions of the guiding conceptual frameworks (theories, models) focussed on linkage and exchange but varied across approaches. Future research should gather KB practitioner and stakeholder perspectives on effective practices to develop KB approaches for healthy aging.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.078
metaresearch head score (Gemma)0.179
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.078
Threshold uncertainty score0.413

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0780.179
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0070.011
Bibliometrics0.0500.043
Science and technology studies0.0040.004
Scholarly communication0.0120.014
Open science0.0050.008
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.912
GPT teacher head0.793
Teacher spread0.120 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
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

Citations46
Published2016
Admission routes3
Has abstractyes

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