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Record W3012586751 · doi:10.1186/s12961-020-0536-9

Conceptualising the initiation of researcher and research user partnerships: a meta-narrative review

2020· review· en· W3012586751 on OpenAlexafffund
Maria Zych, Whitney Berta, Anna R. Gagliardi

Bibliographic record

VenueHealth Research Policy and Systems · 2020
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsCINAHLGeneral partnershipKnowledge translationNarrativeStakeholder engagementHealth careHealth services researchTranslational researchMedicineNursingMedical educationPsychologyKnowledge managementPublic relationsPublic healthPolitical scienceComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Integrated knowledge translation refers to researcher and research user partnerships to co-generate and implement knowledge. This type of partnership may be critical to success in increasing knowledge use and impact, but the conceptualisation of its initiation has not been fully developed. Initiating this type of partnership has proven to be challenging but crucial to its success. The purpose of this study was to conduct a meta-narrative review of partnership initiation concepts, processes, enablers, barriers and outcomes in the disciplines of healthcare and social sciences where examples of researcher and research user partnerships were found. METHODS: Seven research traditions were identified. Three were in the discipline of social sciences (including psychology, education and business) and five were in the discipline of healthcare (including medicine, nursing, public health, health services research). Searches were conducted in MEDLINE, EMBASE, CINAHL, ABI Inform, ERIC, PsychInfo and the Cochrane Library on June 9, 2017. Fifty titles and abstracts were screened in triplicate; data were extracted from three records in duplicate. Narratives comprised of study characteristics and conceptual and empirical findings across traditions were tabulated, summarised and compared. RESULTS: A total of 7779 unique results were identified and 17 reviews published from 1998 to 2017 were eligible. All reviews identified a partnership initiation phase referred to as 'early' or 'developmental', or more vaguely as 'fuzzy', across six traditions - integrated knowledge translation, action research, stakeholder engagement, knowledge transfer, team initiation and shared mental models. The partnership initiation processes, enablers, barriers and outcomes were common to multiple narratives and summarised in a Partnership Initiation Conceptual Framework. Our review revealed limited use or generation of theory in most included reviews, and little empirical evidence testing the links between partnership initiation processes, enablers or barriers, and outcomes for the purpose of describing successful researcher and research user partnership initiation. CONCLUSIONS: Narratives across multiple research traditions revealed similar integrated knowledge translation initiation processes, enablers, barriers and outcomes, which were captured in a conceptual framework that can be employed by researchers and research users to study and launch partnerships. While partnership initiation was recognised, it remains vaguely conceptualised despite lengthy research in several fields of study. Ongoing research of partnership initiation is needed to identify or generate relevant theory, and to empirically establish outcomes and the determinants of those outcomes.

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)0.057
metaresearch head score (Gemma)0.163
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.057
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0570.163
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.008
Bibliometrics0.0200.014
Science and technology studies0.0020.002
Scholarly communication0.0080.011
Open science0.0040.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.975
GPT teacher head0.741
Teacher spread0.234 · 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.

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

Citations67
Published2020
Admission routes2
Has abstractyes

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