What I Wish You Knew: Insights on Burnout, Inertia, Meltdown, and Shutdown From Autistic Youth
Bibliographic record
Abstract
Introduction: Burnout, inertia, meltdown, and shutdown (BIMS) have been identified as important parts of some autistic people’s lives. This study builds on our previous work that offered early academic descriptions of these phenomena, based on the perspectives of autistic adults. Objectives: This study aimed to explore the unique knowledge and insights of eight autistic children and youth to extend and refine our earlier description of burnout, i nertia, and meltdown, with additional exploration of shutdown. We also aimed to explore how these youth cope with these phenomena and what others around them do that make things better or worse, with a hope to glean knowledge to design better supports. Methods: One-to-one interviews were conducted with eight children and youth, who shared their experience with BIMS. To match individual communication strengths of children and youth, we took a flexible approach to interviews, allowing for augmentative communication systems and use of visual images to support verbal interviews, as needed. We conducted a reflexive, inductive thematic analysis, using an iterative process of coding, collating, reviewing, and mapping themes. Findings: Our analysis has identified that these youth describe BIMS as a multi-faceted experience involving emotional, cognitive and physical components. Moreover, these multifaceted experiences are often misunderstood by neurotypical adults, which contributes to inadequate support in managing BIMS. Of the four experiences, these youth identified meltdowns as most common. Conclusion: By gaining first-hand perspectives, we have identified novel insights into BIMS and developed a more holistic understanding of these phenomena. These youths’ descriptions of supportive strategies for BIMS stress the importance of compassion and collaboration from trusted adults. This new knowledge will provide a foundation for how to better support autistic children and youth. Further research is required to develop an understanding of BIMS, especially with respect to how it is experienced by children and youth. Future research should leverage the insights and experiential knowledge of autistic children and youth to co-design support tool(s) for BIMS.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".