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Record W2483444245 · doi:10.5539/ijps.v8n3p98

Which Diagnostic Approach Is More Valid? The DSM or the Rational-Choice Theory of Neurosis

2016· article· en· W2483444245 on OpenAlexvenueno aff
Yacov Rofé

Bibliographic record

VenueInternational Journal of Psychological Studies · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsnot available
Fundersnot available
KeywordsNeurosisPsychologyPsychotherapistExpression (computer science)Empirical researchClinical psychologyCognitive psychologyPsychoanalysisEpistemologyComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

<p>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.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.211
GPT teacher head0.418
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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