Bibliographic record
Abstract
Objective: To examine the relationship between self-inconsistency, alexithymia and Obsessions-Compulsions in neurotic patients and normal control.Methods: 59 patients and 54 control completed The Vancouver Obsessional Compulsive Inventory (VOCI), Self Consistency and Congruence Scale (SCCS), Toronto alexithymia scale (TAS-26), Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS). Results: 1) The difference of VOCI total score among patients with obsessive-compulsive disorder (OCD), anxiety/depression (A/D), and control was significant(111.2±36.0、57.0±33.0、39.4±22.5,F=35.46,P0.05), so did the self-inconsistency score((57.9±11.7、51.4±12.1、36.8±8.8、F=47.33,P0.05). 2) Total score of VOCI was significantly correlated with Self-inconsistency, TAS-DDF, TAS-DIF(r=0.38~0.83, P0.01)in OCD and A/D patients, and was significantly correlated with Self-inconsistency, TAS-DDF, TAS-DIF and TAS-RDR(r=-0.37~0.58, P0.01)in control. 3) Self-inconsistency and TAS-DIF were independent predictive variables of VOCI total score in OCD patients, and in A/D patients and control only the self-inconsistency was the independent predictive variable.Conclusion:The results suggest that Self-inconsistency may make a contribution to Obsessions-Compulsions regardless of the suffering of neurosis, and in OCD patients difficulty in identifying feelings and distinguishing between feelings and bodily sensations may worsen Obsessions-Compulsions when controlling for Self-inconsistency.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".