Exploring the Influence of Interorganization Networks on the Dissemination of National Health Behavior Guidelines
Bibliographic record
Abstract
BACKGROUND: Interorganization partnerships are important for the development and knowledge mobilization of national health behavior guidelines. However, little is known about how to improve the dissemination of guidelines across professional networks. Social network analysis may offer unique insight into the social structure of interorganization networks and provide guidance for how network features may be harnessed for effective dissemination. The objectives of this study were to apply social network analysis to (1) analyze the connectedness of organizations and/or subgroups within a national health behavior guideline network and (2) identify organization attributes associated with influential network positions. METHODS: 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 the connections among health-promoting organizations in Canada. Data were analyzed using UCINET Version 6. Network maps were generated for the interorganization network and its subgroups, and descriptive frequencies were calculated for demographic characteristics. Associations between organization attributes and centrality measures were calculated using Point-Biserial and Spearman rank correlations. RESULTS: Thirty-four organizations completed the survey and reported 228 organizational ties. Density scores for each dissemination network ranged from 1% to 5%, demonstrating the potential for constrained information sharing (ie, dissemination) between organizations. Five attributes were significantly associated with centrality measures, which included location, sector, size, resource allocation, and previous dissemination of sedentary behavior guidelines. CONCLUSIONS: Findings demonstrate the utility of social network analysis for understanding knowledge mobilization across networks and offer guidance for how network features may be leveraged to enhance knowledge mobilization outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".