Negative thoughts in depression: A study in Iran
Bibliographic record
Abstract
The cognitive theory of depression proposes significant relations between negative thoughts and depression. Evidence for the model has been widely observed in Western countries. However, despite the high prevalence of depression in the Middle East (ME), there has been limited research that has evaluated the cognitive profiles of people living in this region, and especially in non-Arab countries like Iran. The current research examined these relationships in Iran. Convenience sampling was used to recruit 80 depressed and 80 non-depressed individuals via advertising in clinics and public areas, respectively. Depression status was checked with a structured interview, the Major Depressive Disorder subscale of the Psychiatric Diagnostic Screening Questionnaire, and the Beck Depression Inventory-II. All participants completed the Automatic Thoughts Questionnaire-Negative to examine the frequency of negative automatic thoughts. Unlike other results from Arab countries, depressed participants indicated significantly more negative thoughts towards self and future compared with the non-depressed group. The results of the present study are consistent with the negativity hypothesis of the cognitive theory of depression. Further research is needed in the in ME, to investigate other hypotheses of this theory in this region. Strengths and limitations of the present study are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".