A Neuro-developmentally Sensitive and Trauma Informed Service Delivery Approach for Child and Youth Mental Health and Psychiatry
Bibliographic record
Abstract
This presentation will introduce the innovative approach to child and youth mental health and psychiatry using the neurosequential model of therapeutics (NMT). This is a neuro-developmentally sensitive and trauma informed approach and acknowledges the importance of early experiences shaping the organization of the brain. An outline of the stress response and its relevance to hyper-arousal and dissociative responses will be discussed as this impacts attachment and the reward neuro-biology. The hierarchy of brain development will be emphasized in the clinical approaches to child psychiatry especially in reference to child maltreatment and neglect. The critical role of sensory integration, self regulation, relational health and cognitive development in treatment planning will be discussed versus the categorical diagnosis of ADHD, autism, bipolar disorder and depression. This has profound economic and psychopharm practice implications in child and youth mental health treatments. Consequently the importance of these concepts in informing public policy for early child development and school mental health literacy will be emphasized. Additionally the outcome of these approaches on the reduction of staff turnover, critical incidents in schools and residential placements will be shared. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".