Knowledge Spillovers through Social Media in Healthcare A Pilot Study in Austria
Bibliographic record
Abstract
This pilot study investigates how social media interactions between healthcare professionals and medical technology companies influence knowledge spillover and medical technology innovation in Austria. Grounded in social network theory and the knowledge spillover theory of entrepreneurship, the research examines how network closure (trust and cohesion) and network brokerage (access to diverse and novel information) shape innovation dynamics. A cross-sectional survey conducted in 2024 was analyzed using social network measures for healthcare professionals and medical technology companies. Results show that urban networks displayed lower density but greater brokerage opportunities, facilitating radical innovation through diverse knowledge flows, while rural networks relied on cohesive, trust-based ties, supporting incremental innovation through established and familiar knowledge exchange. For healthcare professionals, collaboration within trusted local professional communities enhances existing medical practices and reliability in patient care, but may limit access to novel solutions. For medical technology companies, rural contexts require long-term trust with healthcare professionals and support gradual implementation of innovations. Urban networks, by contrast, displayed lower density but greater brokerage opportunities, giving healthcare professionals quicker access to new knowledge and enabling medical technology companies to introduce more radical innovations through various relationships. These results illustrate that digital platforms do not remove spatial differences but instead support them: urban environments reward openness and cross-boundary exchange, while rural environments reward stability and trust. Aligning innovation strategies with these geographical dynamics is crucial for effective collaboration and improved health 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".