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Record W2991222514 · doi:10.1186/s12874-019-0838-1

A review and synthesis of frameworks for engagement in health research to identify concepts of knowledge user engagement

2019· review· en· W2991222514 on OpenAlexafffund
Janet Jull, Laurie A. Davidson, Rachel Dungan, Tram Nguyen, Krista Woodward, Ian D. Graham

Bibliographic record

VenueBMC Medical Research Methodology · 2019
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of OttawaMcMaster University Medical CentreOttawa HospitalQueen's University
FundersCanadian Institutes of Health Research
KeywordsKnowledge managementProcess (computing)Knowledge translationKnowledge sharingUser engagementHealth careComputer scienceMedical educationPsychologyData scienceMedicineWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Engaging those who influence, administer and/or who are active users ("knowledge users") of health care systems, as co-producers of health research, can help to ensure that research products will better address real world needs. Our aim was to identify and review frameworks of knowledge user engagement in health research in a systematic manner, and to describe the concepts comprising these frameworks. METHODS: An international team sharing a common interest in knowledge user engagement in health research used a consensus-building process to: 1) agree upon criteria to identify articles, 2) screen articles to identify existing frameworks, 3) extract, analyze data, and 4) synthesize and report the concepts of knowledge user engagement described in health research frameworks. We utilized the Patient Centered Outcomes Research Institute Engagement in Health Research Literature Explorer (PCORI Explorer) as a source of articles related to engagement in health research. The search includes articles from May 1995 to December 2017. RESULTS: We identified 54 articles about frameworks for knowledge user engagement in health research and report on 15 concepts. The average number of concepts reported in the 54 articles is n = 7, and ranges from n = 1 to n = 13 concepts. The most commonly reported concepts are: knowledge user - prepare, support (n = 44), relational process (n = 39), research agenda (n = 38). The least commonly reported concepts are: methodology (n = 8), methods (n = 10) and analysis (n = 18). In a comparison of articles that report how research was done (n = 26) versus how research should be done (n = 28), articles about how research was done report concepts more often and have a higher average number of concepts (n = 8 of 15) in comparison to articles about how research should be done (n = 6 of 15). The exception is the concept "evaluate" and that is more often reported in articles that describe how research should be done. CONCLUSIONS: We propose that research teams 1) consider engagement with the 15 concepts as fluid, and 2) consider a form of partnered negotiation that takes place through all phases of research to identify and use concepts appropriate to their team needs. There is a need for further work to understand concepts for knowledge user engagement.

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)0.409
metaresearch head score (Gemma)0.200
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4090.200
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0020.010
Insufficient payload (model declined to judge)0.0020.000

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 head0.961
GPT teacher head0.790
Teacher spread0.171 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
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

Citations90
Published2019
Admission routes2
Has abstractyes

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