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Record W2441251642 · doi:10.1186/s12961-016-0118-z

Do knowledge translation (KT) plans help to structure KT practices?

2016· article· en· W2441251642 on OpenAlexafffundabout
Salomon Tchameni Ngamo, Karine Souffez, Catherine Lord, Christian Dagenais

Bibliographic record

VenueHealth Research Policy and Systems · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversité de MontréalCentre de Liaison Sur l'Intervention et la Prévention PsychosocialesInstitut National de Santé Publique du QuébecThe Quebec Population Health Research NetworkUniversité du Québec à Montréal
FundersInstitut National de Santé Publique du Québec
KeywordsOperationalizationKnowledge translationContext (archaeology)Knowledge managementProcess managementPlan (archaeology)Medical educationMedicineComputer scienceBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: A knowledge translation (KT) planning template is a roadmap laying out the core elements to be considered when structuring the implementation of KT activities by researchers and practitioners. Since 2010, the Institut national de santé publique du Québec (INSPQ; Québec Public Health Institute) has provided tools and guidance to in-house project teams to help them develop KT plans. This study sought to identify the dimensions included in those plans and which ones were integrated and how. The results will be of interest to funding agencies and scientific organizations that provide frameworks for KT planning. METHODS: The operationalization of KT planning dimensions was assessed in a mixed methods case study of 14 projects developed at the INSPQ between 2010 and 2013. All plans were assessed (rated) using an analytical tool developed for this study and data from interviews with the planning coordinators. The analytical tool and interview guide were based on eight core KT dimensions identified in the literature. RESULTS: Analysis of the plans and interviews revealed that the dimensions best integrated into the KT plans were 'analysis of the context (barriers and facilitators) and of users' needs', 'knowledge to be translated', 'KT partners', 'KT strategies' and, to a lesser extent, 'overall KT approach'. The least well integrated dimensions were 'knowledge about knowledge users', 'KT process evaluation' and 'resources'. CONCLUSIONS: While the planning coordinators asserted that a plan did not need to include all the dimensions to ensure its quality and success, nevertheless the dimensions that received less attention might have been better incorporated if they had been supported with more instruments related to those dimensions and sustained methodological guidance. Overall, KT planning templates appear to be an appreciated mechanism for supporting KT reflexive practices. Based on this study and our experience, we recommend using KT plans cautiously when assessing project efficacy and funding.

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)0.092
metaresearch head score (Gemma)0.147
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.489

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0920.147
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0060.011
Scholarly communication0.0150.018
Open science0.0030.011
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.002

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 head0.940
GPT teacher head0.789
Teacher spread0.151 · 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 designObservational
DomainMethods
GenreEmpirical

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".

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Citations32
Published2016
Admission routes3
Has abstractyes

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