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Record W2590143404 · doi:10.1186/s13643-017-0430-x

Effective knowledge translation approaches and practices in Indigenous health research: a systematic review protocol

2017· review· en· W2590143404 on OpenAlexafffundabout
Melody E. Morton Ninomiya, Donna Atkinson, Simon Brascoupé, Michelle Firestone, Nicole Robinson, Jeff Reading, Carolyn Ziegler, Raglan Maddox, Janet Smylie

Bibliographic record

VenueSystematic Reviews · 2017
Typereview
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsCanadian Partnership Against CancerUniversity of TorontoCanadian Institutes of Health ResearchCarleton UniversitySimon Fraser UniversitySt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsIndigenousGrey literatureCINAHLMedicinePsycINFOKnowledge translationCritical appraisalInclusion (mineral)MainstreamProtocol (science)Medical educationPublic relationsMEDLINESociologyNursingSocial sciencePolitical sciencePsychological interventionAlternative medicineKnowledge management

Abstract

fetched live from OpenAlex

BACKGROUND: Effective knowledge translation (KT) is critical to implementing program and policy changes that require shared understandings of knowledge systems, assumptions, and practices. Within mainstream research institutions and funding agencies, systemic and insidious inequities, privileges, and power relationships inhibit Indigenous peoples' control, input, and benefits over research. This systematic review will examine literature on KT initiatives in Indigenous health research to help identify wise and promising Indigenous KT practices and language in Canada and abroad. METHODS: Indexed databases including Aboriginal Health Abstract Database, Bibliography of Native North Americans, CINAHL, Circumpolar Health Bibliographic Database, Dissertation Abstracts, First Nations Periodical Index, Medline, National Indigenous Studies Portal, ProQuest Conference Papers Index, PsycInfo, Social Services Abstracts, Social Work Abstracts, and Web of Science will be searched. A comprehensive list of non-indexed and grey literature sources will also be searched. For inclusion, documents must be published in English; linked to Indigenous health and wellbeing; focused on Indigenous people; document KT goals, activities, and rationale; and include an evaluation of their KT strategy. Identified quantitative, qualitative, and mixed methods' studies that meet the inclusion criteria will then be appraised using a quality appraisal tool for research with Indigenous people. Studies that score 6 or higher on the quality appraisal tool will be included for analysis. DISCUSSION: This unique systematic review involves robust Indigenous community engagement strategies throughout the life of the project, starting with the development of the review protocol. The review is being guided by senior Indigenous researchers who will purposefully include literature sources characterized by Indigenous authorship, community engagement, and representation; screen and appraise sources that meet Indigenous health research principles; and discuss the project with the Indigenous Elders to further explore the hazards, wisdom, and processes of sharing knowledge in research contexts. The overall aim of this review is to provide the evidence and basis for recommendations on wise practices for KT terminology and research that improves Indigenous health and wellbeing and/or access to services, programs, or policies that will lead to improved health and wellbeing. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016049787 .

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)0.221
metaresearch head score (Gemma)0.175
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.221
Threshold uncertainty score0.960

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2210.175
Meta-epidemiology (narrow)0.0060.008
Meta-epidemiology (broad)0.0140.012
Bibliometrics0.0230.023
Science and technology studies0.0070.008
Scholarly communication0.0110.012
Open science0.0080.009
Research integrity0.0100.010
Insufficient payload (model declined to judge)0.0670.016

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 head0.641
GPT teacher head0.595
Teacher spread0.046 · 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
Domainnot available
GenreProtocol

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

Citations65
Published2017
Admission routes3
Has abstractyes

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