The 2014 Ontario Child Health Study Emotional Behavioural Scales (OCHS-EBS) Part II: Psychometric Adequacy for Categorical Measurement of Selected DSM-5 Disorders
Bibliographic record
Abstract
Objectives: To compare the reliability and convergent validity of parent assessments from the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID—a structured diagnostic interview) and the Ontario Child Health Study Emotional Behavioural Scales (OCHS-EBS) symptom checklist for classifying conduct disorder (CD), conduct disorder or oppositional defiant disorder (CD-ODD), attention-deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), generalized anxiety disorder (GAD), and separation anxiety disorder (SAD) based on DSM-5 criteria. Methods: Data came from 283 parent-youth dyads aged 9 to 18 years. Parents and youth completed the assessments separately on 2 different occasions 7 to 14 days apart. After converting the OCHS-EBS scale scores to binary disorder classifications, we compare test-retest reliability estimates and use structural equation modelling (SEM) to compare estimates of convergent validity for the same disorders assessed by each instrument. Results: Average test-retest reliabilities based on κ were 0.71 (MINI-KID) and 0.67 (OCHS-EBS). The average β coefficients for 3 latent measures comprising the following indicators—parent perceptions of youth mental health need and impairment, diagnosis of specific disorders based on health professional communications and youth taking prescribed medication, and youth classifications of disorder based on the MINI-KID—were 0.67 (MINI-KID) and 0.69 (OCHS-EBS). Conclusion: The OCHS-EBS and MINI-KID achieve comparable levels of reliability and convergent validity for classifying child psychiatric disorder. The flexibility, low cost, and minimal respondent burden of checklists for classifying disorder make them well suited for studying disorder in the general population and screening in clinical settings.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".