Annual Research Review: Embracing not erasing contextual variability in children’s behavior – theory and utility in the selection and use of methods and informants in developmental psychopathology
Bibliographic record
Abstract
This paper examines the selection and use of multiple methods and informants for the assessment of disruptive behavior syndromes and attention deficit/hyperactivity disorder, providing a critical discussion of (a) the bidirectional linkages between theoretical models of childhood psychopathology and current assessment techniques; and (b) current knowledge concerning the utility of different methods and informants for key clinical goals. There is growing recognition that children's behavior varies meaningfully across situations, and evidence indicates that these differences, in combination with informants' unique perspectives, are at least partly responsible for inter-rater discrepancies in reports of symptomatology. Such data suggest that we should embrace this contextual variability as clinically meaningful information, moving away from models of psychopathology as generalized traits that manifest uniformly across situations and settings, and toward theoretical conceptualizations that explicitly incorporate contextual features, such as considering clinical syndromes identified by different informants to be discrete phenomena. We highlight different approaches to measurement that embrace contextual variability in children's behavior and describe how the use of such tools and techniques may yield significant gains clinically (e.g., for treatment planning and monitoring). The continued development of a variety of feasible, contextually sensitive methods for assessing children's behavior will allow us to determine further the validity of incorporating contextual features into models of developmental psychopathology and nosological frameworks.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".