The DSM5/RDoC debate on the future of mental health research: implication for studies on human stress and presentation of the signature bank
Bibliographic record
Abstract
In 2008, the National Institute of Mental Health (NIMH) announced that in the next few decades, it will be essential to study the various biological, psychological and social "signatures" of mental disorders. Along with this new "signature" approach to mental health disorders, modifications of DSM were introduced. One major modification consisted of incorporating a dimensional approach to mental disorders, which involved analyzing, using a transnosological approach, various factors that are commonly observed across different types of mental disorders. Although this new methodology led to interesting discussions of the DSM5 working groups, it has not been incorporated in the last version of the DSM5. Consequently, the NIMH launched the "Research Domain Criteria" (RDoC) framework in order to provide new ways of classifying mental illnesses based on dimensions of observable behavioral and neurobiological measures. The NIMH emphasizes that it is important to consider the benefits of dimensional measures from the perspective of psychopathology and environmental influences, and it is also important to build these dimensions on neurobiological data. The goal of this paper is to present the perspectives of DSM5 and RDoC to the science of mental health disorders and the impact of this debate on the future of human stress research. The second goal is to present the "Signature Bank" developed by the Institut Universitaire en Santé Mentale de Montréal (IUSMM) that has been developed in line with a dimensional and transnosological approach to mental illness.
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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 | 0.119 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".