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Record W4411329254 · doi:10.1186/s12961-025-01353-5

It is time to acknowledge and act on the importance of power in integrated knowledge translation

2025· article· en· W4411329254 on OpenAlexafffund
Anita Kothari, Bev Holmes, Iain Lang, Chris McCutcheon, Leigha Comer, Ian D. Graham

Bibliographic record

VenueHealth Research Policy and Systems · 2025
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of OttawaLondon Health Sciences CentreOttawa HospitalCanada's Michael Smith Genome Sciences CentreMichael Smith Health Research BCWestern University
FundersCanadian Institutes of Health Research
KeywordsHealth services researchHealth administrationPublic healthPower (physics)Knowledge translationTranslation (biology)Social policyHealth policyMedicinePolitical scienceKnowledge managementComputer scienceNursingLawGeneticsBiology

Abstract

fetched live from OpenAlex

Integrated knowledge translation (IKT) has emerged as an approach to research centered on collaboration between researchers and knowledge users, particularly in health research. There has been a growing focus on power within the IKT literature, especially the concern that overlooking power inequities within IKT partnerships may reproduce forms of knowledge production and dissemination that do not align with IKT's aspirations of shared decision making to produce useful and usable research findings. However, there remain significant gaps in our understanding of how to address and attend to power in IKT. The lack of conceptual precision around power complicates these efforts. In this commentary, we draw on existing literatures that have grappled with the notion of power to sensitize those who study and engage in IKT to the importance of power and to identify helpful ways of thinking about power in IKT. We propose that it is time to not only acknowledge the importance of power in IKT but also to develop empirically based strategies through which the many dimensions of power can be identified and navigated.

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)0.023
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.722
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.747
GPT teacher head0.682
Teacher spread0.065 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
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".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

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