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Record W2522695344

Knowledge Translation Practices Of Health Services Research Organizations In The United States

2012· article· en· W2522695344 on OpenAlexaboutno aff

Bibliographic record

VenueUND Scholarly Commons (University of North Dakota) · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge translationBusinessPublic relationsKnowledge managementPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

Health services research organizations have generated a growing body of literature that focuses on better understanding challenges facing health care delivery. However, their findings do not always reach end users (e.g., policymakers, providers, managers, general public) in ways that are helpful, relevant, or cost-effective despite the availability of numerous resources designed to aid researchers in communicating more effectively. The purpose of this study was to understand better how health services research organizations in the United States communicate their research findings to end users; determine the degree to which they are translating research findings in ways consistent with the empirical evidence; and determine whether organizational characteristics such as university affiliation, organizational specialty, or size explain any variation in responses. Leaders of health services research organizations in the United States responded to a survey about their organizations' knowledge translation practices. The survey instrument and knowledge translation framework were based largely on work conducted by Lavis, Robertson, Woodside, McLeod, and Abelson (2003a) in Canada. Findings from this empirical study expanded the Lavis et al. (2003a) study by setting a baseline for knowledge translation practices, across the research continuum, for health services research organizations in the United States. The data showed that health services research organizations largely communicate about their research in the same manner, regardless of university affiliation, organizational specialty, or size. Research organizations conduct knowledge translation activities throughout the course of their research projects, although in many cases there are gaps between what the literature suggests research organizations optimally should be doing and what they report doing. Notably, these gaps include evaluating knowledge translation activities, utilizing social media tools to extend messaging to end users, engaging with end users throughout the research process, building expectations for knowledge translation into policies and procedures, and investing in knowledge translation development at the organizational level. The findings suggest areas of improvement for health services research organizations. This study observes, however, that increasing knowledge translation capacity will require a cultural shift, and increased collaboration, across the health services research community. Accordingly, this study recommends several action steps. Specifically, health services research organizations should develop knowledge translation expectations through organizational policies and procedures, and invest in capacity building, including training research staff or working with knowledge brokers. Funders should include expectations for knowledge translation in projects, and universities might consider updated promotion and tenure systems that acknowledge and reward translation activities. Bolstering knowledge translation practices as identified in this study, and using the baseline data as a measuring point to evaluate future interventions, contributes to end users successfully receiving research findings in ways that can be useful for decision making, ultimately enhancing the quality of health and health care.

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)0.077
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 score0.923
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.147
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0090.007
Scholarly communication0.0070.004
Open science0.0020.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.632
GPT teacher head0.611
Teacher spread0.021 · 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".

Quick stats

Citations2
Published2012
Admission routes1
Has abstractyes

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