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Record W2149811234 · doi:10.1093/heapro/dar094

Applying the principles of knowledge translation and exchange to inform dissemination of HIV survey results to adolescent participants in South Africa

2012· article· en· W2149811234 on OpenAlexaff
Stephanie Nixon, Marisa Casale, Sabine Flicker, Michael Rogan

Bibliographic record

VenueHealth Promotion International · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsYork UniversityUniversity of Toronto
Fundersnot available
KeywordsKnowledge translationContext (archaeology)Information DisseminationPublic relationsInformation exchangeObligationKnowledge transferDisseminationHealth carePsychologyCompromiseMedical educationMedicinePolitical scienceKnowledge managementSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

It is widely accepted that researchers have an obligation to inform survey participants of research results. However, there is little evidence on the effectiveness of various dissemination strategies. The emerging field of knowledge transfer and exchange (KTE) may offer insight given its focus on techniques to enhance the effectiveness of communicating evidence-based information. To date, KTE has focused primarily on information exchange between researchers and policy-makers as opposed to study participants; however, there are principles that may be relevant in this new context. This gap in the literature becomes even more salient in the context of public health research where research results can reveal particular misunderstandings or shortcomings in knowledge that threaten to severely compromise participants' health. The objective of this article is to describe how KTE principles were used to inform dissemination of results of a self-administered sexual health survey to adolescent study participants in a resource-deprived, peri-urban area of South Africa. Strategies for enhancing two-way information exchange included constructing interactive dissemination sessions led by young, isiZulu fieldworkers. We also employed techniques to create a safe space for dialogue, encouraged the shared ownership of results and crafted targeted messages. Particularly noteworthy was the benefit accrued by the research team through this process of exchange, including novel explanations for study findings and new ideas for future research.

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)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.769
GPT teacher head0.634
Teacher spread0.135 · 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.

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

Citations13
Published2012
Admission routes1
Has abstractyes

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