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Record W3209196033

The use of social network analysis to examine knowledge translation opportunities across organizational networks

2021· article· en· W3209196033 on OpenAlexaboutno aff
Kaitlyn D. Kauffeldt, Heather L. Gainforth, Amy E. Latimer‐Cheung, Guy Faulkner, Jennifer R. Tomasone

Bibliographic record

VenueJournal of Exercise, Movement, and Sport (SCAPPS refereed abstracts repository) · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsSocial network analysisKnowledge translationDisseminationInformation DisseminationSocial network (sociolinguistics)Knowledge managementNetwork analysisOrganizational network analysisDescriptive statisticsPublic relationsSocial mediaPsychologyOrganizational learningPolitical scienceComputer scienceWorld Wide WebEngineering
DOInot available

Abstract

fetched live from OpenAlex

Little is known about how to facilitate the dissemination of national movement (i.e., physical activity, sedentary behaviour, sleep) behaviour guidelines across professional and research networks. Social network analysis (SNA) is the study of relationships among social units (i.e., individuals, groups, or organizations) and is useful for understanding how social structures facilitate or impede knowledge translation (KT) processes. A SNA approach has not been previously used to examine network properties that may influence the dissemination of national movement guidelines across organizational networks. The purpose of this study was to apply SNA to (1) identify organizations in the KT network for Canadian 24-Hour Movement Guidelines for Adults, (2) examine attributes of influential organizations in the KT network, and (3) identify gaps in information exchange across the KT network. Organizations involved in the development and dissemination of the Canadian 24-Hour Movement Guidelines for Adults were invited to complete an online survey to examine relationships among organizations that have disseminated or have the potential to disseminate national-level movement guidelines. Data were analyzed using UCINET v6 to explore network features. A network map was created and descriptive frequencies were calculated. In total, 34 organizations completed the survey and reported a total of 228 organizational ties. The overall network score for density was 0.7% demonstrating a lack of integration across organizations in the KT network. Findings demonstrate the utility of SNA for examining relationships across an organizational network. Practically, these findings may be used to build network capacity for future movement guideline dissemination efforts.Acknowledgments: This work was made possible through funding provided by the Public Health Agency of Canada and the Canadian Society for Exercise Physiology. The authors would like to acknowledge the Canadian 24-Hour Movement Guidelines for Adults Consensus Panel and Knowledge Translation Advisory Committee members for their collaboration and contributions to this project.

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)0.022
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.087
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0220.020
Science and technology studies0.0030.002
Scholarly communication0.0040.007
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.260
GPT teacher head0.460
Teacher spread0.200 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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