Principles and procedures for revising the hierarchical taxonomy of psychopathology.
Bibliographic record
Abstract
Quantitative, empirical approaches to establishing the structure of psychopathology hold promise to improve on traditional psychiatric classification systems. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a framework that summarizes the substantial and growing body of quantitative evidence on the structure of psychopathology. To achieve its aims, HiTOP must incorporate emerging research in a systematic, ongoing fashion. In this article, we describe the historical context and grounding of the principles and procedures for revising the HiTOP framework. Informed by strengths and shortcomings of previous classification systems, the proposed revisions protocol is a formalized system focused around three pillars: (a) prioritizing systematic evaluation of quantitative evidence by a set of transparent criteria and processes, (b) balancing stability with flexibility, and (c) promoting inclusion over gatekeeping in all aspects of the process. We detail how the revisions protocol will be applied in practice, including the scientific and administrative aspects of the process. Additionally, we describe areas of the HiTOP structure that will be a focus of early revisions and outline challenges for the revisions protocol moving forward. The proposed revisions protocol is designed to ensure that the HiTOP framework reflects the current state of scientific knowledge on the structure of psychopathology and fulfils its potential to advance clinical research and practice. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.423 | 0.618 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".