Mapping collaborative relations among Canada's chronic disease prevention organizations
Bibliographic record
Abstract
Objectives There is converging evidence from the fields of sociology, organizational science and management that organizations gain an advantage from being embedded in a dense network of collaborative relations. We used network analysis to map the collaborations between organizations with a chronic disease prevention mandate in Canada. Approach The data for this analysis were collected as part of the 2010 Public Health Organizational Capacity Study (PHORCAST) census. PHORCAST is a repeat census of all public health organizations engaged in primary chronic disease prevention (CDP) at the regional, provincial, territorial and national levels in Canada. Respondent organizations (n = 207) were asked to use a name generator approach to list organizations with which they had collaborations, resulting in the identification of 1,322 organizations linked through 2,815 collaborative relations. Optimized sociograms of the resulting collaborative network were produced using structural network analysis (Cytoscape 3.1.0 software). Results Of the 1,322 organizations identified, 1,038 (78%) are interconnected in one single component which spans all provinces and territories. We computed degree and betweenness centrality for all organizations comprising this main component and analyzed mean provincial scores. The results show that CDP organizations' density and interconnectedness are much higher in Manitoba, Saskatchewan and the Maritime provinces. Interconnectedness was weakest in British Columbia and Alberta. We also used two complementary sociogram optimization algorithms to map out the structure of the CDP network. Visual analysis of optimized sociograms suggests that CDP organizations in Saskatchewan and those with a federal or multi-province mandate are structurally different from the Canadian average. Conclusion In this study we identified clusters of organizations that have either a central position or a bridging function in the network of collaborative relations. These results may provide important clues about the link between provincial organizational capacity for chronic disease prevention and population health outcomes. Key messages Public health capacity should not be conceived as the sum of discrete organization's capacities but as a complex ecology of organizations whose influence is shaped by the way they are interconnected The method we developped allows to draw an actual map of organizational collaborations in the field of public health interventions at the national level
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.023 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".