Restraint and Critical Incident Reduction Following Introduction of the Neurosequential Model of Therapeutics (NMT)
Bibliographic record
Abstract
Children with developmental trauma are at risk for severe and complex behavioral problems, often requiring long-term residential and day treatment. The Neurosequential Model of Therapeutics (NMT) is a developmentally sensitive approach to clinical work with a capacity-building component focusing on attachment, the impact of maltreatment and trauma, and emerging concepts in developmental psychology, neuroscience and traumatology. Research has demonstrated its effectiveness with trauma-exposed populations. NMT training may help providers working with trauma-exposed youth prevent critical incidents and reduce restraints. Restraint and critical incident data were obtained from 10 organizations providing residential and/or day-treatment services following exposure to, or certification in, the NMT. Data from the Pre-NMT Introduction period through to the Maintenance phase of NMT Certification were used to examine changes in restraints and critical incidents across phases of NMT exposure/certification. Multilevel logistic regression models suggested that NMT exposure and/or certification was associated with significant reductions in restraints and critical incidents. Reductions were sustained throughout the Maintenance phase. Estimates of potential staff hour and financial savings associated with these reductions are discussed. Implementation of the NMT in residential and day-treatment settings may result in staff, behavioral health provider, and organization-level changes that reduce critical incidents and restraint use.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".