Exploring the link between educational leadership and mental health in schools. A narrative synthesis
Bibliographic record
Abstract
This article offers a narrative synthesis of recent empirical research focusing on the intersection between educational leadership and mental health initiatives within schools, a field that remains underexplored despite its growing relevance. Drawing on four studies published between 2018 and 2024, the narrative synthesis examines the role of educational leadership in supporting mental health initiatives in schools for both students and teachers. The selected studies employ qualitative and mixed-methods designs and span diverse educational contexts in the United States, the United Kingdom, Canada, and Northern Ireland. Findings indicate an area with significant potential for growth in the preparation of school leaders to address mental health issues or call attention to limited systemic collaboration among educational stakeholders. However, the studies also highlight promising practices, including participatory leadership approaches and knowledge brokerage roles that can enhance mental health support within schools. Methodological limitations related to sample size, generalizability, and study heterogeneity are acknowledged. By identifying key challenges and opportunities, this narrative synthesis advances understanding of educational leadership’s role in effectively addressing mental health challenges at the institutional level. It underscores the need for improved pre-service training, stronger interprofessional collaboration, and further empirical research to inform policy and practice in this emerging area.
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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".