What Happened? Exploring the Relation between Traumatic Stress and Provisional Mental Health Diagnoses for Children and Youth
Bibliographic record
Abstract
Objective: Traumatic stress can impact behaviours and neurological functioning of children and youth, with symptoms appearing similar to behaviours associated with psychiatric diagnoses (Siegfried et al., 2016). This study sought to examine the link between provisional diagnoses and trauma in a sample of children/youth receiving mental health services. Methods: A sample of 6649 children/youth (59% males) aged 4 - 18 years (Mage = 11.99, SD = 3.57) receiving services from 45 mental health agencies in Ontario were assessed using the interRAI Child and Youth Mental Health (ChYMH) instrument (Stewart et al., 2015a). We examined the interRAI Traumatic Life Events Collaborative Action Plan (CAP; Stewart et al., 2015b) and provisional diagnoses of attention-deficit/hyperactivity disorder (ADHD), anxiety disorders, reactive attachment disorder (RAD), mood disorders, substance-related disorders, and sleep disorders. Results: Compared to boys, girls were more likely to trigger the interRAI Traumatic Life Events CAP and to have a provisional diagnosis of anxiety, mood, and sleep disorders. Boys were more likely to have a provisional diagnosis of ADHD than girls. Multiple logistic regression analyses indicated that boys diagnosed with substance-related disorders had 1.79 higher odds of triggering the interRAI Traumatic Life Events CAP. ADHD, anxiety disorders, RAD, and mood disorders were also each significant predictors of potential traumatic stress regardless of sex. Conclusions/Implications: Findings suggest that several provisional diagnoses were significantly related to potential traumatic stress. Clinicians may find value in assessing for trauma, asking the question “What happened?” when confirming a psychiatric diagnosis in order to determine the best plan of care.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".