Educational leaders and school-based mental health: a social network analysis of knowledge brokerage
Bibliographic record
Abstract
This study examines the relational dynamics of knowledge brokerage among educational leaders in implementing multi-tiered systems of support (MTSS) for mental health promotion in schools. Recognizing the critical link between student mental health and academic achievement, schools are increasingly expected to provide comprehensive mental health supports. Utilizing social network analysis (SNA), this research explores the information-seeking behaviors of educational leaders within a public school system in British Columbia, Canada. By mapping the relational connections among school and district leaders, the study identifies how these leaders, as knowledge brokers, navigate and influence educational policies and practices related to MTSS. The findings reveal latent interaction patterns that can either facilitate or impede the flow of essential information, highlighting potential areas for strategic intervention. The analysis demonstrates the importance of leadership in fostering collaboration and ensuring the effective integration of mental health initiatives into school communities. Additionally, the study underscores the complex interactions through which knowledge is exchanged and mobilized, emphasizing the need for systems to enhance relational ties and promote collaborative leadership. This research contributes to the scholarship and practice of educational leadership by advancing the understanding of how social networks and knowledge brokerage can support the successful implementation of MTSS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".