Narrative Inquiry: Leading the Implementation of a Self-Organized School-Wide Trauma Informed Approach
Bibliographic record
Abstract
Developmental trauma as a result of adverse childhood experiences (ACEs) before the age of 18 has cumulative effects on health and development across a lifespan. ACEs can have a negative impact on a child’s success in school with a likelihood of impairments and negative health and developmental consequences. Given that schools are places where children and youth spend a significant amount of time, it is important to explore the ways in which trauma informed supports can be offered to children and families. This dissertation explores the leadership style most aligned with the implementation processes of a self-organized school-wide trauma informed approach (SWTIA). Principals and vice-principals (P/VPs) are key figures in leading professional teams in change efforts yet their role in the implementation of a self-organized SWTIA has not been studied. My dissertation presents the first Canadian research that identifies, explores, and describes how five P/VPs led the implementation of a self-organized SWTIA. I used narrative inquiry as both methodology and method. I conducted semi-structured interviews and invited arts-based responses to communicate further meaning of experiences. Findings suggested that adaptive leadership behaviours align with implementation of a self- organized SWTIA to support change to practice at individual and organization levels. Furthermore, findings indicated a need for an implementation framework to address internal and external barriers and facilitators to implementation and to guide the complex process of individual and organizational change. The practice-oriented implications of this dissertation pertain to education leaders’ understanding and use of an implementation framework and adaptive leadership practices when implementing a self-organized SWTIA. Implications for research include exploring adaptive leadership within other self-organized SWTIA, the use of additional change mechanisms such as storytelling as a leadership tool during implementation, and the use of implementation frameworks that are applicable in ways that support the health and well-being of children and youth who have experienced developmental trauma and are at risk for impairment.
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 | 0.029 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".