Classifying child and adolescent psychiatric disorder by problem checklists and standardized interviews
Bibliographic record
Abstract
This paper discusses the need for research on the psychometric adequacy of self-completed problem checklists to classify child and adolescent psychiatric disorder based on proxy assessments by parents and self-assessments by adolescents. We put forward six theoretical arguments for expecting checklists to achieve comparable levels of reliability and validity with standardized diagnostic interviews for identifying child psychiatric disorder in epidemiological studies and clinical research. Empirically, the modest levels of test-retest reliability exhibited by standardized diagnostic interviews - 0.40 to 0.60 based on kappa - should be achievable by checklists when thresholds or cut-points are applied to scale scores to identify a child with disorder. The few studies to conduct head-to-head comparisons of checklists and interviews in the 1990s concurred that no construct validity differences existed between checklist and interview classifications of disorder, even though the classifications of youth with psychiatric disorder only partially overlapped across instruments. Demonstrating that self-completed problem checklists can classify disorder with similar reliability and validity as standardized diagnostic interviews would provide a simple, brief, flexible way to measuring psychiatric disorder as both a categorical or dimensional phenomenon as well as dramatically lowering the burden and cost of assessments in epidemiological studies and clinical research.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".