The Development of Preliminary HiTOP Internalizing Spectrum Scales
Bibliographic record
Abstract
As part of a broader project to create a comprehensive self-report measure for the Hierarchical Taxonomy of Psychopathology consortium, we developed preliminary scales to assess internalizing symptoms. The item pool was created in four steps: (a) clarifying the range of content to be assessed, (b) identifying target constructs to guide item writing, (c) developing formal definitions for each construct, and (d) writing multiple items for each construct. This yielded 430 items assessing 57 target constructs. Responses from a heterogeneous scale development sample ( N = 1,870) were subjected to item-level factor analyses based on polychoric correlations. This resulted in 39 scales representing a total of 213 items. The psychometric properties of these scales replicated well across the development sample and an independent validation sample ( N = 496 adults). Internal consistency analyses established that most scales assess relatively narrow forms of psychopathology. Structural analyses demonstrated the presence of a strong general factor. Additional analyses of the 35 nonsexual dysfunction scales revealed a replicable four-factor structure with dimensions we labeled Distress, Fear, Body Dysmorphia, and Mania. A final set of analyses established that the internalizing scales varied widely—and consistently—in the strength of their associations with neuroticism and extraversion.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".