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Record W4283072332 · doi:10.1186/s12961-022-00877-4

A scoping review to identify and describe the characteristics of theories, models and frameworks of health research partnerships

2022· review· en· W4283072332 on OpenAlexafffund
Brenda J. Tittlemier, J Cooper, Dawn Steliga, Roberta L. Woodgate, Kathryn M. Sibley

Bibliographic record

VenueHealth Research Policy and Systems · 2022
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsResearch CanadaCanadian Institutes of Health ResearchUniversity of ManitobaGeorge & Fay Yee Centre for Healthcare InnovationManitoba Health
FundersCanadian Institutes of Health Research
KeywordsHealth services researchCINAHLHealth careKnowledge managementCritical appraisalHealth administrationHealth informaticsProcess (computing)General partnershipData scienceManagement scienceComputer scienceMedicinePublic healthNursingAlternative medicinePolitical sciencePsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Engaging users of health research, namely knowledge users, as partners in the research process may to lead to evidence that is more relevant to the users. This may optimize the uptake of evidence in healthcare practice, resulting in improved health outcomes or more efficient healthcare systems. However, barriers to involving knowledge users in the research process exist. Theories, models and frameworks may help guide the process of involving knowledge users and address barriers to engaging with knowledge users in research; however, there is little evidence identifying or describing the theories, models and frameworks of health research partnerships. OBJECTIVES: Identify and describe theories, models and frameworks of health research partnerships. Report on concepts of knowledge user engagement represented in identified theories, models and frameworks. METHODS: We conducted a scoping review. Database (MEDLINE, Embase, CINAHL, PCORI) and ancestry and snowball searches were utilized. Included articles were written in English, published between January 2005 and June 2021, specific to health, a research partnership, and referred to a theory, model or framework. No critical appraisal was conducted. We developed a coding framework to extract details related to the publication (e.g. country, year) and theory, model or framework (e.g. intended users, theoretical underpinning, methodology, methods of development, purpose, concepts of knowledge user engagement). One reviewer conducted data extraction. Descriptive statistics and narrative synthesis were utilized to report the results. RESULTS: We identified 21 874 articles in screening. Thirty-nine models or frameworks were included in data analysis, but no theory. Two models or frameworks (5%) were underpinned by theory. Literature review was the method (n = 11, 28%) most frequently used to develop a model or framework. Guiding or managing a partnership was the most frequently reported purpose of the model/framework (n = 14, 36%). The most represented concept of knowledge user engagement was principles/values (n = 36, 92%). CONCLUSIONS: The models and frameworks identified could be utilized by researchers and knowledge users to inform aspects of a health research partnership, such as guidance or implementation of a partnership. Future research evaluating the quality and applicability of the models and frameworks is necessary to help partners decide which model or framework to implement.

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
gemmaMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.111
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
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 score0.419
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.006
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 head0.948
GPT teacher head0.717
Teacher spread0.231 · 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.

Metaresearch

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

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

Citations23
Published2022
Admission routes2
Has abstractyes

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