Which Diagnostic Approach Is More Valid? The DSM or the Rational-Choice Theory of Neurosis
Bibliographic record
Abstract
This article challenges the validity of the DSM-III to exclude neurosis, a decision that has led the DSM to become “an expanding list of disease, from a few dozen disorders in the first edition to well over 200” (Grinker, 2010, p. 169; see also Warelow & Holmes, 2011). It points out the unanimous consensus that the best diagnostic approach would be a theory that can account for the development and treatment of certain diagnostic categories and, at the same time, provide measurable criteria that can distinguish them from other behaviors. Accordingly, it shows that a new theory, the Rational-Choice Theory of Neurosis (RCTN) (Rofé, 2000, 2010, 2016; Rofé & Rofé, 2013, 2015), which despite profound differences is similar to psychoanalysis in several fundamental respects, can offer practical diagnostic criteria that differentiate neurosis from other disorders. Three types of evidence, including a review of research literature, case studies and a new study that directly examined the validity of RCTN’s diagnostic criteria, support the validity of neurosis. The greatest advantage of RCTN’s diagnostic approach is not only is based on empirical evidence instead of the consensus of biased researchers. Rather, their main contribution is that it emerged out of a theory that succeeded to integrate research and clinical data pertaining to the development and treatment of neurosis.
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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 | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| 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".