Introduction to the Special Issue. A Dozen Years of Demonstrating That Informant Discrepancies are More Than Measurement Error: Toward Guidelines for Integrating Data from Multi-Informant Assessments of Youth Mental Health
Bibliographic record
Abstract
Validly characterizing youth mental health phenomena requires evidence-based approaches to assessment. An evidence-based assessment cannot rely on a “gold standard” instrument but rather, batteries of instruments. These batteries include multiple modalities of instrumentation (e.g., surveys, interviews, performance-based tasks, physiological readings, structured clinical observations). Among these instruments are those that require soliciting reports from multiple informants: People who provide psychometrically sound data about youth mental health (e.g., parents, teachers, youth themselves). The January 2011 issue of the Journal of Clinical Child and Adolescent Psychology (JCCAP) included a Special Section devoted to the most common outcome of multi-informant assessments of youth mental health, namely discrepancies across informants’ reports (i.e., informant discrepancies). The 2011 JCCAP Special Section revolved around a critical question: Might informant discrepancies contain data relevant to understanding youth mental health (i.e., domain-relevant information)? This Special Issue is a “sequel” to the 2011 Special Section. Since 2011, an accumulating body of work indicates that informant discrepancies often contain domain-relevant information. Ultimately, we designed this Special Issue to lay the conceptual, methodological, and empirical foundations of guidelines for integrating multi-informant data when informant discrepancies contain domain-relevant information. In this introduction to the Special Issue, we briefly review the last 12 years of research and theory on informant discrepancies. This review highlights limitations inherent to the most commonly used strategies for integrating multi-informant data in youth mental health. We also describe contributions to the Special Issue, including articles about informant discrepancies that traverse multiple content areas (e.g., autism, implementation science, measurement validation, suicide).
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.031 | 0.018 |
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".