Deconstruction in Psychiatry: Are We Missing the Forest for the Trees?
Bibliographic record
Abstract
Background Although the field of psychiatry has recently seen significant advancement in neurobiological knowledge, we have not seen corresponding improvements in clinical practice. In response to this stagnation, some researchers have turned to concepts such as deconstruction by incorporating programs like the Research Domain Criteria (RDoC) into psychiatric research. Methods This review examines the role of deconstruction in psychiatry and analyzes efforts to apply a dimensional approach to psychopathology. Results In this article, we argue that deconstruction rejects current medical knowledge in favor of an ill-fitting dimensional approach, placing symptoms in a broader spectrum rather than treating symptoms as individual characteristics of categorical diagnoses. We contend that psychiatry is not unique among medical fields and that a categorical approach continues to be most effective for achieving clinical success. While heterogeneity in clinical presentations complicates efforts to better understand psychiatric disorders, it is not an inherent impediment to progress and does not warrant redefining psychiatric illness. Conclusions Deconstruction in psychiatry is unlikely to yield any improvement in clinical practice. Ultimately, further study is needed to better understand the heterogeneity found in psychiatric disease.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.089 |
| Scholarly communication | 0.012 | 0.030 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.018 |
| 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".