Impact of Trauma on Brain Morphology & Maladaptive School Behaviors
Bibliographic record
Abstract
Wendi Trummert, DrOT, OTR/L, the collaborating clinician for this project, works with students in a self-contained program. Wendi wanted to know how childhood trauma affects brain structure and morphology and how it is linked to behaviors seen in children affected by trauma. A synthesis of all articles looking at the brain reveals differences in both structure and function in the brains of individuals exposed to childhood trauma versus those not exposed (e.g. Daniels, Lamke, Gaebler, Walter, & Scheel, 2013; McGowan et al., 2009; Saleh et al., 2017). A synthesis of articles looking at maladaptive behaviors finds that those often seen in children affected by trauma, including aggression, emotional dysregulation, decreased executive functioning, and hyporeactivity may be linked to these brain changes and may explain why traditional behavioral approaches are often ineffective with this population (e.g. Briggs-Gowan et al., 2010; Lemmey et al., 2001; Shields & Cicchetti, 1998). It is recommended this information be disseminated to educators and others who work with children exposed to trauma to increase understanding and promote appropriate supports.\nA toolkit including a presentation, pamphlet, and conversational sound bytes were created for the clinician to increase knowledge and combat bias about problem behaviors in children affected by trauma amongst educators and coworkers. Insiders’ perspectives were included to generate empathy, help guide the best type of support for additional student services, and reduce occurrences of inappropriate interventions for maladaptive behaviors. Options and resources to effectively address maladaptive behaviors should be provided to educators after they are presented with this information.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".