A Cross-Cultural Study of the Cognitive Model of Depression: Cognitive Experiences Converge between Egypt and Canada
Bibliographic record
Abstract
INTRODUCTION: Models of depression that arise in the West need to be examined in other regions of the world. This study examined a set of foundational hypotheses generated by Beck's cognitive model of depression among depressed individuals in Egypt and Canada. METHOD: We recruited 29 depressed and 29 non-depressed Egyptians and compared their results with those of 35 depressed and 38 non-depressed Canadians. Depression status was ascertained using a structured interview, scores on the Beck Depression Inventory, and scores on the Psychiatric Diagnostic Screening Questionnaire. Participants completed questionnaires designed to measure the frequency of negative and positive automatic thoughts (ATQ-N, BHS, and ATQ-P), and dysfunctional attitudes (DAS). RESULTS: Depressed individuals in both countries had significantly more negative thoughts about self and future, greater frequency of dysfunctional attitudes, and diminished positive self-thoughts in comparison to non-depressed individuals. Egyptians generally showed significantly more dysfunctional attitudes than their Canadian counterparts. DISCUSSION: The four hypotheses that were tested were supported among the depressed Egyptian sample, which is consistent with the cognitive model. Implications for the cognitive-behavioral model and treatment for this group of sufferers 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".