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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".