The Importance and Limits of Harm in Identifying Mental Disorder
Bibliographic record
Abstract
The In Review articles in this issue on normality and disorder by Dr Rachel Cooper and Dr Derek Bolton explore the importance of a value component of harm in the concept of mental disorder. They focus on the Diagnostic and Statistical Manual of Mental Disorder's clinical significance criterion, requiring that symptoms cause significant distress or role impairment, as the expression of the harm component. As Dr Bolton argues, harm in the form of distress or role impairment has always been intimately tied to the concept of disorder and treatment decisions; as Dr Cooper argues, without the harm requirement, any disliked anomaly may be labelled a disorder. Moreover, as Cooper argues, a harm requirement is not incompatible with a natural kinds approach to distinguishing among disorders or to a categorical approach to disorder; the lack of zones of rarity on the harm continuum does not preclude categorical underlying causal processes. However, neither paper systematically develops arguments regarding the other component of disorder, the requirement that the harm must be caused by underlying dysfunction. The dysfunction component distinguishes disorders from the many other negative conditions in life. Cooper's identification of dysfunction with symptom severity ignores the fact that normal suffering can be severe, and Bolton's attempt to encompass risk of harm within harm yields an implausibly expansive conception of disorder. While the harm component is essential, clarification of the dysfunction component of the concept of disorder, pursued in part 2 of this In Review in the December 2013 issue, is also essential to establishing a coherent and plausibly limited domain of psychiatric disorder within the broader arena of harmful conditions.
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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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".