MétaCan
Menu
Back to cohort
Record W1926257277 · doi:10.1186/1748-5908-10-s1-a80

Engaging public health decision makers in partnership research

2015· article· en· W1926257277 on OpenAlexaffabout
Maureen Dobbins, Robyn Traynor

Bibliographic record

VenueImplementation Science · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGeneral partnershipKnowledge translationRelevance (law)Public healthHealth services researchMedicineHealth informaticsHealth administrationPublic relationsHealth policyPolitical scienceKnowledge managementNursingComputer science

Abstract

fetched live from OpenAlex

Involving decision makers in collaborative research partnerships can help increase the relevance and timeliness of the research question, and ensure the results are more readily applied in practice. These partnerships offer great benefits but also unique challenges. We will discuss some of these challenges, as identified from our recent study (Canadian Institutes of Health Research FRN 101867, 126353) and the growing literature on engaging decision-makers in knowledge translation (KT) research. We will also recommend strategies for ensuring a successful partnership. We collaborated with three Canadian public health departments to enhance capacity for and facilitate organizational contexts conducive to evidence-informed decision making (EIDM). The research team and decision-maker partners jointly developed the research questions and KT strategies, tailored to each partner's organizational needs and goals. Intervention effectiveness was assessed via quantitative (online survey, in-person assessment) and qualitative (interviews, reflective journal entries, case study notes) data; this discussion has been informed, in part, by the qualitative analysis. Identified challenges include: unpredictable practice settings and a change in priorities over time; time and staff workload; decision maker research knowledge and prior experience; and balancing applied research with rigorous scientific practice. To mitigate these challenges, we recommend: sustaining open and ongoing communication to maintain momentum and reinforce commitment; identifying a key contact to help facilitate and promote the study; obtaining formal approval on timelines, goals, communication, and role expectations; and establishing a mutual understanding of the research and decision-making processes. A strong relationship between researchers and decision makers, built on mutual respect, trust, and understanding, is critical for successful partnerships. This paradigm is continuing to gain recognition as an effective approach to KT research. Our discussion on possible challenges (and their associated solutions) will help ensure both researchers and decision makers are able to enter more productive partnerships.

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)0.112
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1120.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.008
Science and technology studies0.0030.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.983
GPT teacher head0.857
Teacher spread0.127 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueImplementation ScienceSame topicHealth Policy Implementation ScienceFrench-language works237,207