Reframing failure: lessons from educational leaders facilitating multi-tiered systems of support
Bibliographic record
Abstract
Purpose This paper explores the failure experiences of educational leaders facilitating the implementation of multi-tiered systems of support (MTSS) for student mental health. By examining these leaders’ involvement within a professional learning network (PLN), this study highlights how failure can lead to iterative learning and improved school interventions. Design/methodology/approach The study employs a qualitative approach, using semi-structured interviews with 19 participants, including system leaders, school leaders and health professionals. Data were analyzed according to a inductive–deductive approach, drawing from the integrated Promoting Action on Research Implementation in Health Services framework and Edmondson’s Spectrum of Reasons for Failure. Findings Educational leaders encountered failure in two primary areas: process inadequacy and task challenge. Failures included fragmented implementation of mental health interventions, lack of coherent data infrastructure and challenges in providing consistent support across diverse contexts, particularly in rural areas. Failure experiences were linked to the complexity of facilitating multi-tiered interventions and navigating systemic constraints. Originality/value This study reframes failure as a potentially generative element in the facilitation of MTSS. It finds that PLNs can serve as a platform for educational leaders to collectively learn from failures. Educational leaders and policymakers could use the findings to inform the implementation and evaluation approaches used for MTSS. In particular, PLNs can be leveraged to foster collaboration and adaptive leadership practices, enabling leaders to develop more effective mental health interventions for students.
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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 | 0.040 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| 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".