Measuring Interactions among Research Grant Recipients through Social Network Analysis: Insights into Evaluating and Improving Research Collaborations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
IntroductionThis project aimed to assess how a major collaborative research grant initiative affected interactions among grant recipients. The Collaborative Research Grant Initiative: Mental Wellness in Seniors and Persons with Disabilities (CRGI) was funded by a grant awarded by the Alberta Minister of Human Services to Alberta Health Services-Addiction & Mental Health. The CRGI had two main goals. First, it funded both academic and practitioner-driven research designed to assist individuals living with a mental illness and disabilities to maximize their independence in the community. In addition, the CRGI was meant to increase awareness of Alberta-based research, and foster collaboration and knowledge exchange, among policy makers, researchers, and community agencies. Developing research collaborations across multiple organizations, disciplines, and locations is a complex challenge and requires significant support (Craven & Bland, 2006). Researchers have argued that we must demonstrate effective knowledge exchange practices in order to better understand what forms of support are effective under real world conditions (Norman & Huerta, 2006).The CRGI steering committee agreed that a first step toward enhancing collaboration within Alberta was to increase awareness among practitioners and researchers in the mental health field. Several information sessions and knowledge exchange events were held to support potential research grant applicants through the CRGI application process, to encourage collaboration, and to disseminate research results. Activities important to the Ministry included capacity building within community agencies funded by the Ministry; capacity building within the Ministry itself; and multi-sectoral collaboration. The CRGI steering committee also decided to evaluate the effectiveness of the workshops using social network analysis. This paper focuses on the social network analysis results, based on data collected from 26 CRGI participants who were surveyed. The project aimed to assess effects of the CRGI on collaboration between grant recipients and their knowledge of one another's work. Therefore we also aimed to illustrate which components of the CRGI were affective in achieving the Ministry's goals. The survey asked grant recipients for demographic information, through which CRGI activities they had met, and about their collaborative activities with one another before and after winning a CRGI grant and engaging in related events. We include information regarding support activities below and further details regarding the grants, projects awarded funding, and the activities that were undertaken to support their collaboration are available at the Alberta Addiction & Mental Health Research Partnership Program website (http://www.mentalhealthresearch.ca).Social Network AnalysisSocial network analysis (SNA) is the study of the structure of relations between actors, people, or organizations who have the capacity to take action (Wasserman & Faust, 1994; Scott, 2000). Key SNA principles include: actors are interdependent; resources such as information can be transferred between actors via the nature of their relationships; the form that relationships take between actors can limit or enable the capacity for individual action and; models of networks- their structure-are considered as regularly occurring patterns of relationships between actors within a network (Wasserman & Faust, 1994). In SNA, structure is often illustrated using sociograms or graphs where points (typically actors) are connected by lines (relations) (Scott, 2000). In other words, a sociogram is a graph that represents the people within a social structure and their connections to their peers. These graphs can be effective in visualizing what a network looks like by allowing relations to become visually observable in situations where the observation is typically theoretical or ephemeral.Social network analysis is a useful means to examine interdisciplinary collaboration (Stokols et ah, 2003; Godley, Barron, & Sharma, 2011; Godley, Sharkey, & Weiss, 2013). …
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it