Integrating multi-informant reports of youth mental health: A construct validation test of Kraemer and colleagues’ (2003) Satellite Model
Bibliographic record
Abstract
Accurately assessing youth mental health involves obtaining reports from multiple informants who typically display low levels of correspondence. This low correspondence may reflect situational specificity. That is, youth vary as to where they display mental health concerns and informants vary as to where and from what perspective they observe youth. Despite the frequent need to understand and interpret these informant discrepancies, no consensus guidelines exist for integrating informants’ reports. The path to building these guidelines starts with identifying factors that reliably predict the level and form of these informant discrepancies, and do so for theoretically and empirically relevant reasons. Yet, despite the knowledge of situational specificity, few approaches to integrating multi-informant data are well-equipped to account for these factors in measurement, and those that claim to be well-positioned to do so have undergone little empirical scrutiny. One promising approach was developed roughly 20 years ago by Kraemer and colleagues (2003). Their Satellite Model leverages principal components analysis (PCA) and strategic selection of informants to instantiate situational specificity in measurement, namely components reflecting variance attributable to the context in which informants observe behavior (e.g., home/non-home), the perspective from which they observe behavior (e.g., self/other), and behavior that manifests across contexts and perspectives (i.e., trait). The current study represents the first construct validation test of the Satellite Model. A mixed-clinical/community sample of 134 adolescents and their parents completed six parallel surveys of adolescent mental health. Adolescents also participated in a series of simulated social interactions with research personnel trained to act as same-age, unfamiliar peers. A third informant (unfamiliar untrained observer) viewed these interactions and completed the same surveys as parents and adolescents. We applied the Satellite Model to each set of surveys and observed high internal consistency estimates for each of the six-item trait (α = 0.90), context (α = 0.84), and perspective (α = 0.83) components. Scores reflecting the trait, context, and perspective components displayed distinct patterns of relations to a battery of criterion variables that varied in the context, perspective, and source of measurement. The Satellite Model instantiates situational specificity in measurement and facilitates unifying conceptual and measurement models of youth mental health.
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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 | 0.054 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".