A scoping review on the neurosequential model
Bibliographic record
Abstract
Child trauma alters brain development and has profound negative consequences for health and wellbeing. The Neurosequential Model (NM) is a neurodevelopmentally-informed framework for supporting youth impacted by trauma that focuses on improving self-regulation and relational engagement. The NM is not an intervention itself, but it does provide a structured approach to assess the needs of a child, create an individualized treatment plan, and deliver tailored interventions. As there are no published reviews that comprehensively examine the NM literature, we conducted a scoping review with the goal of identifying what is known about the NM. Our scoping review identified 38 academic journal articles, 20 book chapters, 12 pieces of graduate research and 10 pieces of gray literature on the NM. Publication trends indicate that the NM is primarily used by mental health providers and educators. Most empirical studies on the NM focused on the impact of the model on children exposed to trauma, although some articles focused on the impact of NM training on professionals or the impact of implementing the NM on organizations. Almost all studies reported positive impacts of using the NM, including improving self-regulation and post-traumatic stress symptoms over time. Few studies had a comparison group that was not exposed to the NM. The primary benefits of the NM appear to be related to improving understanding of how trauma can lead to behavioral issues by altering brain development, and by helping providers to select and deliver interventions that support the unique neurodevelopmental needs of clients. • The first comprehensive review of publications on the Neurosequential Model • The model helps providers create customized and engaging treatment plans. • The model is mainly used with children exposed to trauma, often in child welfare. • The model is often used by mental health and social service providers, and educators. • Most studies report the model improves mental health and trauma symptoms.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".