Increasing Diagnostic Emphasis on Negative Affective Dysfunction: Potentially Negative Consequences for Psychiatric Classification and Diagnosis
Bibliographic record
Abstract
Maladaptive experiences of negative mood states and difficulties regulating them, collectively referred to here as negative affective dysfunction, are linked robustly to many disorders. Despite negative affective dysfunction being a nonspecific psychopathology feature, the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders ( DSM–5) introduced new (a) disorders and (b) features to existing disorders intended to capture manifestations of negative affective dysfunction. This theoretical article highlights why these additions may exacerbate issues concerning disorder overlap and differential diagnosis. Specific examples are provided to support this viewpoint, including potential consequences of emphasizing negative affective dysfunction within the diagnostic criteria for attention-deficit/hyperactivity disorder. Although researchers likely will continue to disagree about how to best classify negative affective dysfunction (e.g., using dimensions vs. categories), I argue that we can reach common ground as a field by recognizing that caution is needed when proposing new DSM–5 additions to capture nonspecific psychopathology features.
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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 | 0.027 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".