Bibliographic record
Abstract
Present study was conducted to compare cognitive distortions and emotion regulations in people with depressive disorder, obsessive-compulsive disorder and normal individuals.In terms of goal, this research was applied, and in terms of method, it was expost facto or causal-comparative.50 depressed patients, 50 obsessive-compulsive order patients and 50 normal individuals were selected as samples of this research by purposive sampling method.The data was analyzed using analysis of variance (ANOVA) and Tukey test.In terms of cognitive distortions and emotion regulation, there was significant difference between depressed patients and normal individuals and also significant difference was observed between obsessivecompulsive disorder patients and normal individuals but there was no significant difference between depressed patients and obsessive-compulsive disorder patients.The research suggested that people with depressive and obsessive-compulsive disorders, have significant distortions in recognition of themselves compared to normal people and also use more negative emotional strategies in facing with stressful events in their live, so, they experience more anxiety and stress.This study indicated that people with obsessivecompulsive and depressive disorders have more cognitive distortions than normal people and use maladaptive strategies of emotion regulation in coping with negative events.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.933 | 0.889 |
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; the direct Gemma label and the distilled Codex classifier 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".