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Record W4389299723 · doi:10.1186/s13012-023-01320-0

Knowledge translation strategies to support the sustainability of evidence-based interventions in healthcare: a scoping review

2023· review· en· W4389299723 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueImplementation Science · 2023
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsOttawa HospitalUniversity of OttawaDalhousie UniversityUniversity of Alberta
FundersUniversity of AlbertaFaculty of Nursing, University of AlbertaCanadian Institutes of Health ResearchWomen and Children's Health Research InstituteChildren's Health Research Institute
KeywordsSustainabilityKnowledge translationStakeholderProcess managementHealth administrationPsychological interventionHealth careMedicineKnowledge managementInclusion (mineral)Health services researchMedical educationManagement scienceNursingBusinessComputer sciencePublic relationsPublic healthPolitical sciencePsychologyEngineeringEcology

Abstract

fetched live from OpenAlex

BACKGROUND: Knowledge translation (KT) strategies are widely used to facilitate the implementation of EBIs into healthcare practices. However, it is unknown what and how KT strategies are used to facilitate the sustainability of EBIs in institutional healthcare settings. OBJECTIVES: This scoping review aimed to consolidate the current evidence on (i) what and how KT strategies are being used for the sustainability of EBIs in institutional healthcare settings; (ii) the reported KT strategy outcomes (e.g., acceptability) for EBI sustainability, and (iii) the reported EBI sustainability outcomes (e.g., EBI activities or component of the intervention continue). METHODS: We conducted a scoping review of five electronic databases. We included studies describing the use of specific KT strategies to facilitate the sustainability of EBIs (more than 1-year post-implementation). We coded KT strategies using the clustered ERIC taxonomy and AIMD framework, we coded KT strategy outcomes using Tierney et al.'s measures, and EBI sustainability outcomes using Scheirer and Dearing's and Lennox's taxonomy. We conducted descriptive numerical summaries and a narrative synthesis to analyze the results. RESULTS: The search identified 3776 studies for review. Following the screening, 25 studies (reported in 27 papers due to two companion reports) met the final inclusion criteria. Most studies used multi-component KT strategies for EBI sustainability (n = 24). The most common ERIC KT strategy clusters were to train and educate stakeholders (n = 38) and develop stakeholder interrelationships (n = 34). Education was the most widely used KT strategy (n = 17). Many studies (n = 11) did not clearly report whether they used different or the same KT strategies between EBI implementation and sustainability. Seven studies adapted KT strategies from implementation to sustainability efforts. Only two studies reported using a new KT strategy for EBI sustainability. The most reported KT strategy outcomes were acceptability (n = 10), sustainability (n = 5); and adoption (n = 4). The most commonly measured EBI sustainability outcome was the continuation of EBI activities or components (n = 23), followed by continued benefits for patients, staff, and stakeholders (n = 22). CONCLUSIONS: Our review provides insight into a conceptual problem where initial EBI implementation and sustainability are considered as two discrete time periods. Our findings show we need to consider EBI implementation and sustainability as a continuum and design and select KT strategies with this in mind. Our review has emphasized areas that require further research (e.g., KT strategy adaptation for EBI sustainability). To advance understanding of how to employ KT strategies for EBI sustainability, we recommend clearly reporting the dose, frequency, adaptations, fidelity, and cost of KT strategies. Advancing our understanding in this area would facilitate better design, selection, tailored, and adapted use of KT strategies for EBI sustainability, thereby contributing to improved patient, provider, and health system 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.

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)0.039
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.566
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.012
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.971
GPT teacher head0.837
Teacher spread0.134 · 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