128 The influence of social networks on knowledge transfer within and between healthcare organizations: a scoping review
Bibliographic record
Abstract
<h3>Objectives</h3> Social network analysis focuses on the relationships between people and structures that form through their interactions. Research in the field has shown that people can be influenced by their social networks to embrace new practices that influence their lives. Social network theory centers on the role of relationships in the creation, spread, and utilization of knowledge. The aims of this scoping review were to systematically map the social network analysis research conducted both within (intra) and between (inter) organizations in healthcare settings, to understand the prevalence of the network intervention types enacted thus far, and to identify existing gaps in the literature. As social network analysis could be conceptualized and operationalized in a variety of ways within health research, a scoping review of social network analysis is warranted. <h3>Method</h3> A scoping review of published research was conducted using Medline, ABI Inform and PsycInfo databases in 2022. We searched terms synonymous with social network analysis, knowledge transfer and organization. Studies eligible for inclusion included theoretical or conceptual papers and quantitative, qualitative and mixed-methods studies in order to consider different measures and aspects of social network analysis and knowledge transfer. Articles were excluded if they did not address the transfer of knowledge in a healthcare organizational context, focused on the spread of disease, discussed the use of social media in public spheres or focused on molecular networks. Data was abstracted on article characteristics, study design, study location, setting of the intervention, professional role(s) observed, how social network theory/analysis was utilized, whether it observed inter and/or intra organizational knowledge transfer, what knowledge was transferred, the type(s) of network intervention observed or proposed, and theories, models or frameworks mentioned. <h3>Results</h3> We included 95 studies in this review: 8 were theoretical studies, 43 were quantitative, 15 were qualitative and 29 utilized mixed methods. The use of sociometric surveys or questionnaires was the most common method used. Most of the studies were set in hospitals and its units. Although most studies looked at multiple professions as they aimed to conduct whole network analyses of the settings, the professionals most focused on were physicians. The types of knowledge studied by the researchers included primarily practitioner knowledge, process knowledge and resource knowledge. There was a relatively even split between studies that focused on networks between organizations (inter), within organizations (intra) and studies that looked at both. Most researchers incorporated social network theories and analysis in the conceptual framework, data collection, data analysis, and interpretation of findings phases. Network interventions utilizing segmentation approaches were less prevalent in the healthcare context. <h3>Conclusions</h3> This review contributes to the management and knowledge translation literatures as it provides an overview of the existing healthcare focused publications that have referred to social network analysis in the organizational context. It also outlines gaps in where further research may be conducted. A future study may include the studies that were excluded at the full text stage as they were not situated in healthcare settings to provide a more comprehensive overview of the influence of social networks on knowledge transfer within and amongst organizations. Few studies used social network analysis to study inter-professional collaboration even though many observed interactions between different professions, thus indicating an area of further study. The review also outlined the diversity in the ways social network theories and analysis may be utilized in research and moving knowledge whether it be as a theoretical framework, a method, to guide analysis or interpretation or as an intervention.
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How this classification was reachedexpand
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".